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社会网络

2021-08-18T08:37:59Z

Kuangmy：/* 中观层面 */

{{Network Science}}

A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.

社会网络（Social Network）是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用社会网络分析（Social Network Analysis），以确定本地及全球模式，定位有影响力的实体，并审查网络动态。

Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>

社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份社交关系图（Sociogram）以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />

==概览==

[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]

The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].

社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于人类学、生物学、传播学、经济学、地理学、信息科学、组织学、社会心理学、社会学和社会语言学。
==历史==

In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>

在19世纪90年代晚期，埃米尔·涂尔干（Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />

[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref name=":11">For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref name=":12">See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref name=":13">Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref name=":14">Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref name=":15">{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref name=":16">Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref name=":17">Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref name=":18">{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref name=":19">{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref name=":20">Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref name=":21">Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref name=":22">Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref name=":23">Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref name=":24">Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref name=":25">Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>

20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。<ref name="jscott" /><ref name=":11" /><ref name=":12" /> 在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见社会测量sociometry）。在人类学中，社会网络理论的基础是布罗尼斯拉夫·马林诺夫斯基（Bronislaw Malinowski）<ref name=":13" /> 、阿尔弗雷德·拉德克利夫-布朗（Alfred Radcliffe-Brown）<ref name=":14" /><ref name=":15" /> 和克洛德·列维-斯特劳斯（Claude Lévi-Strauss）<ref name=":16" /> 的理论和人种学著作。包括约翰·A·巴恩斯（John A. Barnes）<ref name=":17" /> 、J·克莱德·米切尔（J. Clyde Mitchell）和伊丽莎白·博特·斯皮利厄斯（Elizabeth Bott Spillius）<ref name=":18" /><ref name=":19" /> 在内的一群与马克斯·格拉克曼（Max Gluckman）和曼彻斯特学派（Manchester School）有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。<ref name="jscott" /> 与此同时，英国人类学家纳德尔（S.F. Nadel）发展出了一种社会结构理论，对后来的网络分析产生重要影响影响。<ref name=":20" /> 在社会学方面，塔尔科特·帕森斯（Talcott Parsons）的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。<ref name=":21" /><ref name=":22" /> 后来，社会学家彼得·布劳 Peter Blau的社会交换论为分析社会单位之间的关系提供了强大的动力。<ref name=":23" /><ref name=":24" /><ref name=":25" />

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref name=":26">{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref name=":27">Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader" /><ref name=":28">Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref name=":29">Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref name=":30">Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>

到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家哈里森·怀特（Harrison White）和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的查尔斯·堤利（Charles Tilly），还有发表了六度分隔理论论文的斯坦利·米尔格拉姆（Stanley Milgram）。<ref name=":26" /> 马克·格兰诺维特（Mark Granovetter）<ref name="Introduction for the French Reader" /> 和巴里·威尔曼（Barry Wellman）<ref name=":27" /> 是怀特以前的学生，他们阐述并支持对社交网络的分析。<ref name="Introduction for the French Reader" /><ref name=":28" /><ref name=":29" /><ref name=":30" />

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如邓肯·瓦茨（Duncan J. Watts）、艾伯特-拉斯洛·巴拉巴西（Albert-László Barabási）、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。

==分析层次==

[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]

中心性

In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref name=":31">Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref name=":32">{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref name=":33">{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref name=":34">{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].

一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。<ref name=":31" /><ref name=":32" /> 随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析<ref name=":33" />（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。<ref name="Kadu12" /><ref name=":34" /> 局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。

===微观层面===
At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.

在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。

'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].

二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。

'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.

三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。<ref name="Kadu12" /> 在弗里茨 · 海德 Fritz Heider的平衡理论 Balance Theory中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。

'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.

行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。<ref name="Jone11" /> 这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。

'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref name=":35">{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>

子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。<ref name=":35" />

===中观层面===
In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref name=":36">{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>

一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。<ref name=":36" />

[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]

'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref name=":37">{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>

组织: 正规组织是为共同目标分配任务的社会团体。<ref name="Riketta, M. 2007" /> 关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。<ref name="Riketta, M. 2007" /> 网络群体的在线实验证明了通过各种干预优化群体层面协调的方法，这其中包括在群体中加入自主代理。<ref name=":37" />

'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref name=":38">{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>

随机分布的网络: 社会网络的社会网络的指数随机图模型（Exponential random graph models）在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。<ref name=":38" />[[File:Scale-free network sample.png|thumb|right|图5：Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref name=":39">{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref name=":40">Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.

无标度网络（A Scale-free Network）：无标度网络网络是一个度分布（Degree Distribution）遵循幂律（Power Law）(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。<ref name=":39" /> 无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“枢纽（Hubs）” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是聚集系数（General Characteristic）分布，它随着节点度的增加而减少。该分布也遵循幂律。<ref name=":40" /> 上述的网络演化的巴拉巴西模型就是无标度网络的一个例子。

===宏观层面===

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].

宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。

[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]

'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).

大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。

'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref name=":41">{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>

复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括网络拓扑 Network Topology的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见复杂性科学、动力系统和混沌理论），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的同配性（Assortativity）或非同配性、社区结构（见随机分块模型 Stochastic Block Model）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、三重显著性特征（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和随机图（Random Graph），并没有表现出这些特征。<ref name=":41" />

==理论联系==

=== 输入的理论 ===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref name=":42">{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是图论、平衡理论、社会比较论，以及最近的社会认同方法。<ref name=":42" />

===本土理论===

Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].

很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref name=":43">{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>

异质性理论的基础是在一项研究中发现，更多的弱关系可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。<ref name=":43" />
==结构洞==

In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />

在网络的背景下，社会资本存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。<ref name="Burt 2004" /> 这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。<ref name="Burt 1992" />当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个结构洞。<ref name="Burt 1992" /> 因此，一个连接结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。<ref name="Burt 1992" />

Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.

富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。<ref name="Burt 1992" /> 例如，在商业网络中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。

==研究集群==

===传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.

传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。

===社区===

In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.

在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。

===复杂网络===

[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].

复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。

Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.

双相演化理论（Dual-phase Evolution）机制解释了连接性的时间变化如何促进社会网络结构的形成。

===犯罪网络===

In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

在'''犯罪学 Criminology和'''城市社会学 Urban sociology中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。

===创新扩散理论===

[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.

创新扩散理论（Diffusion of Innovations）研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。

===人口学===

In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

在人口学方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。

===经济社会学===

The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref name=":44">{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>

社会学领域几乎完全关注社会互动的结果网络。更狭义地说，经济社会学通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。<ref name=":44" />

===卫生保健===

Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>

社会网络分析越来越多地被纳入卫生保健分析——不仅在流行病学研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。

===人类生态学===

[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref name=":45">Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref name=":46">Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>

人类生态学是研究人类与其自然环境、社会环境和环境建构之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。<ref name=":45" /><ref name=":46" />

===语言与语言学===

Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.

语言学和语言学的研究，特别是进化语言学（Evolutionary Linguistics），关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。

===文学网络===

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref name=":47">{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref name=":48">{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref name=":49">{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.

在文学体系的研究中，网络分析被阿海尔（Anheier）、格尔哈兹（Gerhards）和罗姆（Romo）<ref name=":47" /> 、努伊（De Nooy）<ref name=":48" /> 和Senekal<ref name=":49" /> 应用于研究文学如何运作的各个方面。其基本前提是将文-佐哈尔（Even-Zohar）著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。

===组织研究===

Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref name=":50">{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref name=":51">{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref name=":52">{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>

研究正式或非正式组织关系、组织传播、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。<ref name=":50" />研究发现，组织内网络对组织承诺（Organizational Commitment）<ref name=":51" /> 、组织认同<ref name="Jone11" /> 、人际公民行为有影响。<ref name=":52" />

=== 社会资本 ===
[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref name=":53">(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref name=":54">(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref name=":55">(Claridge, 2018).</ref>

社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。<ref name=":53" />这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。<ref name=":54" /> 认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。<ref name=":55" />

[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref name=":56">{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref name=":57">{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>

社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。<ref name=":56" /><ref name=":57" /> 在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。<ref name="GaudeulGiannetti2013" />

=== 广告===

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===网络位置和收益===

In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

===社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.

计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在电脑中介传播（computer-mediated communication）环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着电子商务的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。

In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>

此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />

== 参阅 ==
* [[Bibliography of sociology]] 社会学参考书目

* [[Business networking]] 商业社交

* [[Collective network]] 集体网络

* [[International Network for Social Network Analysis]] 国际社会网络学会

* [[Network society]] 网络社会

* [[Network theory]] 网络理论

*[[Network science]] 网络科学

* [[Semiotics of social networking]] 社交网络符号学

* [[Scientific collaboration network]] 科学合作网络

* [[Social network analysis]] 社会网络分析

* [[Social network (sociolinguistics)]] 社会网络

* [[Social networking service]] 社会网络服务

* [[Social web]] 社交网络

* [[Structural fold]] 结构折叠

==参考文献==

{{reflist}}

==拓展阅读==

* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}

* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}

* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}

* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}

* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}

* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}

* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}

* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}

* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}

* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}

* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}

* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}

==外部链接==

===组织===
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''

* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''

* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''

* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''

* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''

* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}

===课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg

* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle

* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti

* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)

===数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]

* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]

* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]

* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]

* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]

* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]

* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]

* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]

==编者推荐==
集智俱乐部：[https://swarma.org/?p=18646 社会网络分析的四个维度：研究方法、应用和软件工具综述 | 网络科学论文速递25篇]

[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。

{{DEFAULTSORT:Social Network}}

[[Category:Social networks| ]]

[[Category:Communication theory]]

[[Category:Community building]]

[[Category:Complex systems theory]]

[[Category:Network theory]]

[[Category:Organizational theory]]

[[Category:Self-organization]]

[[Category:Social information processing]]

[[Category:Social systems]]

[[Category:Sociological terminology]]

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[[Category:待整理页面]]

社会网络

2021-08-18T08:11:15Z

Kuangmy：/* 参阅 */

{{Network Science}}

A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.

社会网络（Social Network）是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用社会网络分析（Social Network Analysis），以确定本地及全球模式，定位有影响力的实体，并审查网络动态。

Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>

社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份社交关系图（Sociogram）以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />

==概览==

[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]

The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].

社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于人类学、生物学、传播学、经济学、地理学、信息科学、组织学、社会心理学、社会学和社会语言学。
==历史==

In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>

在19世纪90年代晚期，埃米尔·涂尔干（Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />

[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref name=":11">For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref name=":12">See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref name=":13">Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref name=":14">Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref name=":15">{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref name=":16">Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref name=":17">Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref name=":18">{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref name=":19">{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref name=":20">Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref name=":21">Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref name=":22">Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref name=":23">Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref name=":24">Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref name=":25">Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>

20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。<ref name="jscott" /><ref name=":11" /><ref name=":12" /> 在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见社会测量sociometry）。在人类学中，社会网络理论的基础是布罗尼斯拉夫·马林诺夫斯基（Bronislaw Malinowski）<ref name=":13" /> 、阿尔弗雷德·拉德克利夫-布朗（Alfred Radcliffe-Brown）<ref name=":14" /><ref name=":15" /> 和克洛德·列维-斯特劳斯（Claude Lévi-Strauss）<ref name=":16" /> 的理论和人种学著作。包括约翰·A·巴恩斯（John A. Barnes）<ref name=":17" /> 、J·克莱德·米切尔（J. Clyde Mitchell）和伊丽莎白·博特·斯皮利厄斯（Elizabeth Bott Spillius）<ref name=":18" /><ref name=":19" /> 在内的一群与马克斯·格拉克曼（Max Gluckman）和曼彻斯特学派（Manchester School）有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。<ref name="jscott" /> 与此同时，英国人类学家纳德尔（S.F. Nadel）发展出了一种社会结构理论，对后来的网络分析产生重要影响影响。<ref name=":20" /> 在社会学方面，塔尔科特·帕森斯（Talcott Parsons）的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。<ref name=":21" /><ref name=":22" /> 后来，社会学家彼得·布劳 Peter Blau的社会交换论为分析社会单位之间的关系提供了强大的动力。<ref name=":23" /><ref name=":24" /><ref name=":25" />

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref name=":26">{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref name=":27">Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader" /><ref name=":28">Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref name=":29">Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref name=":30">Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>

到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家哈里森·怀特（Harrison White）和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的查尔斯·堤利（Charles Tilly），还有发表了六度分隔理论论文的斯坦利·米尔格拉姆（Stanley Milgram）。<ref name=":26" /> 马克·格兰诺维特（Mark Granovetter）<ref name="Introduction for the French Reader" /> 和巴里·威尔曼（Barry Wellman）<ref name=":27" /> 是怀特以前的学生，他们阐述并支持对社交网络的分析。<ref name="Introduction for the French Reader" /><ref name=":28" /><ref name=":29" /><ref name=":30" />

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如邓肯·瓦茨（Duncan J. Watts）、艾伯特-拉斯洛·巴拉巴西（Albert-László Barabási）、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。

==分析层次==

[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]

中心性

In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref name=":31">Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref name=":32">{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref name=":33">{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref name=":34">{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].

一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。<ref name=":31" /><ref name=":32" /> 随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析<ref name=":33" />（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。<ref name="Kadu12" /><ref name=":34" /> 局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。

===微观层面===
At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.

在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。

'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].

二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。

'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.

三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。<ref name="Kadu12" /> 在弗里茨 · 海德 Fritz Heider的平衡理论 Balance Theory中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。

'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.

行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。<ref name="Jone11" /> 这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。

'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref name=":35">{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>

子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。<ref name=":35" />

===中观层面===
In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>

一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。

[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]

'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>

组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。

'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>

随机分布的网络: 社会网络的'''指数随机图模型 Exponential random graph models'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。

[[File:Scale-free network sample.png|thumb|right|图5：Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.

'''[[无标度网络]] A Scale-free Network''': 无标度网络网络是一个'''度分布 Degree Distribution'''遵循'''幂律 Power Law'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''枢纽 Hubs'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''集聚系数 General Characteristic'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。

===宏观层面===

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].

宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。

[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]

'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).

大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。

'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>

复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''网络拓扑 Network Topology'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''同配性 Assortativity'''或非同配性、社区结构（见'''随机分块模型 Stochastic Block Model'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''三重显著性特征'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''随机图 Random Graph'''，并没有表现出这些特征。

==理论联系==

=== 输入的理论 ===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''平衡理论 Balance theory'''、社会比较论，以及最近的社会认同方法。

===本土理论===

Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].

很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>

异质性理论的基础是在一项研究中发现，更多的'''弱关系 Weak Tie'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。

==结构洞==

In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />

在网络的背景下，'''社会资本 Social Capital'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''结构洞 Structural Hole'''。因此，一个'''连接'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。

Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.

富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''商业社交 Business networking'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。

==研究集群==

===传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.

传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。

===社区===

In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.

在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。

===复杂网络===

[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].

复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。

Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.

'''双相演化理论 Dual-phase Evolution'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。

===犯罪网络===

In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

在'''犯罪学 Criminology和'''城市社会学 Urban sociology中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。

===创新扩散理论===

[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.

'''创新扩散理论 Diffusion of Innovations'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。

===人口学===

In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

在'''人口学 Demography'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。

===经济社会学===

The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>

社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''经济社会学 Economic Sociology'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。

===卫生保健===

Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>

社会网络分析越来越多地被纳入卫生保健分析——不仅在'''流行病学 Epidemiology'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。

===人类生态学===

[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>

'''人类生态学 Human ecology'''是研究人类与其自然环境、社会环境和'''建成环境 Built environment'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。

===语言与语言学===

Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.

语言学和语言学的研究，特别是'''演化语言学 Evolutionary Linguistics'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。

===文学网络===

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.

在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。

===组织研究===

Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>

研究正式或非正式组织关系、'''组织沟通 Organizational Communication'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''组织承诺 Organizational Commitment'''、组织认同、人际公民行为有影响。

=== 社会资本 ===
[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>

社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。

[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>

社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。

=== 广告===

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===网络位置和收益===

In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

===社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.

计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在电脑中介传播（Computer-mediated Communication）环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着电子商务的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。

In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>

此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />

== 参阅 ==
* [[Bibliography of sociology]] 社会学参考书目

* [[Business networking]] 商业社交

* [[Collective network]] 集体网络

* [[International Network for Social Network Analysis]] 国际社会网络学会

* [[Network society]] 网络社会

* [[Network theory]] 网络理论

*[[Network science]] 网络科学

* [[Semiotics of social networking]] 社交网络符号学

* [[Scientific collaboration network]] 科学合作网络

* [[Social network analysis]] 社会网络分析

* [[Social network (sociolinguistics)]] 社会网络

* [[Social networking service]] 社会网络服务

* [[Social web]] 社交网络

* [[Structural fold]] 结构折叠

==参考文献==

{{reflist}}

==拓展阅读==

* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}

* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}

* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}

* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}

* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}

* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}

* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}

* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}

* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}

* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}

* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}

* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}

==外部链接==

===组织===
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''

* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''

* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''

* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''

* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''

* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}

===课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg

* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle

* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti

* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)

===数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]

* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]

* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]

* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]

* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]

* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]

* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]

* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]

==编者推荐==
集智俱乐部：[https://swarma.org/?p=18646 社会网络分析的四个维度：研究方法、应用和软件工具综述 | 网络科学论文速递25篇]

[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。

{{DEFAULTSORT:Social Network}}

[[Category:Social networks| ]]

[[Category:Communication theory]]

[[Category:Community building]]

[[Category:Complex systems theory]]

[[Category:Network theory]]

[[Category:Organizational theory]]

[[Category:Self-organization]]

[[Category:Social information processing]]

[[Category:Social systems]]

[[Category:Sociological terminology]]

[[Category:Sociological theories]]

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[[Category:待整理页面]]

社会网络

2021-08-18T07:45:23Z

Kuangmy：/* 编者推荐 */

{{Network Science}}

A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.

社会网络（Social Network）是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用社会网络分析（Social Network Analysis），以确定本地及全球模式，定位有影响力的实体，并审查网络动态。

Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>

社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份社交关系图（Sociogram）以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />

==概览==

[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]

The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].

社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于人类学 、生物学、传播学、经济学、地理学、信息科学、组织学、社会心理学、社会学和社会语言学。

==历史==

In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>

在19世纪90年代晚期，埃米尔·涂尔干（Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />

[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref>For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref>See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref>Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref>Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref>{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref>Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref>Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref>{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref>{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref>Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref>Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref>Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref>Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref>Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref>Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>

20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见'''社会测量 Sociometry）'''。在人类学中，社会网络理论的基础是'''布罗尼斯拉夫·马林诺夫斯基 Bronislaw Malinowski'''，'''阿尔弗雷德·拉德克利夫-布朗 Alfred Radcliffe-Brown'''和'''克洛德·列维-斯特劳斯 Claude Lévi-Strauss'''的理论和人种学著作。包括'''约翰·A·巴恩斯 John A. Barnes'''、'''J·克莱德·米切尔 J. Clyde Mitchell'''和'''伊丽莎白·博特·斯皮利厄斯 Elizabeth Bott Spillius'''在内的一群与'''马克斯·格拉克曼 Max Gluckman'''和'''曼彻斯特学派 Manchester School'''有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。在社会学方面，'''塔尔科特·帕森斯 Talcott Parsons'''的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。后来，社会学家'''彼得·布劳 Peter Blau'''的'''社会交换论 Social Exchange Theory'''为分析社会单位之间的关系提供了强大的动力。

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref>{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref>Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader"/><ref>Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref>Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref>Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>

到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家'''哈里森·怀特 Harrison White'''和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的'''查尔斯·堤利 Charles Tilly'''，还有发表了六度分隔理论论文的'''斯坦利·米尔格拉姆 Stanley Milgram'''。'''马克·格兰诺维特 Mark Granovetter'''和'''巴里·威尔曼 Barry Wellman'''是怀特以前的学生，他们阐述并支持对社交网络的分析。

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如'''[[邓肯·瓦茨 Duncan J. Watts]]'''、'''[[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]]'''、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。

==Levels of analysis 分析层次==

[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]

Centrality中心性

In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref>Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref>{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref>{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref>{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].

一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。

===Micro level 微观层面===

At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.

在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。

'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].

二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。

'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.

三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。在'''弗里茨 · 海德 Fritz Heider'''的'''平衡理论 Balance Theory'''中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。

'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.

行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。

'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref>{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>

子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。

===Meso level 中观层面===

In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>

一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。

[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]

'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>

组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。

'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>

随机分布的网络: 社会网络的'''指数随机图模型 Exponential random graph models'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。

[[File:Scale-free network sample.png|thumb|right|图5：Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.

'''[[无标度网络]] A Scale-free Network''': 无标度网络网络是一个'''度分布 Degree Distribution'''遵循'''幂律 Power Law'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''枢纽 Hubs'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''集聚系数 General Characteristic'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。

===Macro level 宏观层面===

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].

宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。

[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]

'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).

大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。

'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>

复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''网络拓扑 Network Topology'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''同配性 Assortativity'''或非同配性、社区结构（见'''随机分块模型 Stochastic Block Model'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''三重显著性特征'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''随机图 Random Graph'''，并没有表现出这些特征。

==Theoretical links 理论联系==

=== Imported theories 输入的理论 ===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''平衡理论 Balance theory'''、社会比较论，以及最近的社会认同方法。

===Indigenous theories 本土理论===

Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].

很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>

异质性理论的基础是在一项研究中发现，更多的'''弱关系 Weak Tie'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。

==Structural holes 结构洞==

In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />

在网络的背景下，'''社会资本 Social Capital'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''结构洞 Structural Hole'''。因此，一个'''连接'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。

Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.

富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''商业社交 Business networking'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。

==Research clusters 研究集群==

===Communication 传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.

传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。

===Community 社区===

In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.

在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。

===Complex networks 复杂网络===

[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].

复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。

Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.

'''双相演化理论 Dual-phase Evolution'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。

===Criminal networks 犯罪网络===

In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

在'''犯罪学 Criminology和'''城市社会学 Urban sociology中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。

===Diffusion of innovations 创新扩散理论===

[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.

'''创新扩散理论 Diffusion of Innovations'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。

===Demography 人口学===

In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

在'''人口学 Demography'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。

===Economic sociology 经济社会学===

The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>

社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''经济社会学 Economic Sociology'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。

===Health care 卫生保健===

Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>

社会网络分析越来越多地被纳入卫生保健分析——不仅在'''流行病学 Epidemiology'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。

===Human ecology 人类生态学===

[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>

'''人类生态学 Human ecology'''是研究人类与其自然环境、社会环境和'''建成环境 Built environment'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。

===Language and linguistics 语言与语言学===

Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.

语言学和语言学的研究，特别是'''演化语言学 Evolutionary Linguistics'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。

===Literary networks 文学网络===

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.

在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。

===Organizational studies 组织研究===

Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>

研究正式或非正式组织关系、'''组织沟通 Organizational Communication'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''组织承诺 Organizational Commitment'''、组织认同、人际公民行为有影响。

===Social capital 社会资本===

[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>

社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。

[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>

社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。

=== Advertising 广告===

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===Network position and benefits 网络地位和利益===

In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

===Social media 社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.

计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在电脑中介传播（Computer-mediated Communication）环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着电子商务的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。

In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>

此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />

=='''参阅'''==

* [[Bibliography of sociology]] 社会学参考书目

* [[Business networking]] 商业社交

* [[Collective network]] 集体网络

* [[International Network for Social Network Analysis]] 国际社会网络学会

* [[Network society]] 网络社会

* [[Network theory]] 网络理论

*[[Network science]] 网络科学

* [[Semiotics of social networking]] 社交网络符号学

* [[Scientific collaboration network]] 科学合作网络

* [[Social network analysis]] 社会网络分析

* [[Social network (sociolinguistics)]] 社会网络

* [[Social networking service]] 社会网络服务

* [[Social web]] 社交网络

* [[Structural fold]] 结构折叠

==参考文献==

{{reflist}}

==拓展阅读==

* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}

* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}

* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}

* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}

* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}

* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}

* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}

* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}

* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}

* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}

* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}

* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}

==外部链接==

===组织===
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''

* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''

* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''

* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''

* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''

* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}

===课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg

* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle

* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti

* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)

===数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]

* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]

* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]

* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]

* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]

* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]

* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]

* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]

==编者推荐==
集智俱乐部：[https://swarma.org/?p=18646 社会网络分析的四个维度：研究方法、应用和软件工具综述 | 网络科学论文速递25篇]

[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。

{{DEFAULTSORT:Social Network}}

[[Category:Social networks| ]]

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[[Category:Community building]]

[[Category:Complex systems theory]]

[[Category:Network theory]]

[[Category:Organizational theory]]

[[Category:Self-organization]]

[[Category:Social information processing]]

[[Category:Social systems]]

[[Category:Sociological terminology]]

[[Category:Sociological theories]]

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社会网络

2021-08-18T07:43:02Z

Kuangmy：

{{Network Science}}

A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.

社会网络（Social Network）是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用社会网络分析（Social Network Analysis），以确定本地及全球模式，定位有影响力的实体，并审查网络动态。

Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>

社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份社交关系图（Sociogram）以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />

==概览==

[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]

The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].

社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于人类学 、生物学、传播学、经济学、地理学、信息科学、组织学、社会心理学、社会学和社会语言学。

==历史==

In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>

在19世纪90年代晚期，埃米尔·涂尔干（Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />

[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref>For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref>See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref>Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref>Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref>{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref>Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref>Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref>{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref>{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref>Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref>Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref>Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref>Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref>Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref>Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>

20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见'''社会测量 Sociometry）'''。在人类学中，社会网络理论的基础是'''布罗尼斯拉夫·马林诺夫斯基 Bronislaw Malinowski'''，'''阿尔弗雷德·拉德克利夫-布朗 Alfred Radcliffe-Brown'''和'''克洛德·列维-斯特劳斯 Claude Lévi-Strauss'''的理论和人种学著作。包括'''约翰·A·巴恩斯 John A. Barnes'''、'''J·克莱德·米切尔 J. Clyde Mitchell'''和'''伊丽莎白·博特·斯皮利厄斯 Elizabeth Bott Spillius'''在内的一群与'''马克斯·格拉克曼 Max Gluckman'''和'''曼彻斯特学派 Manchester School'''有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。在社会学方面，'''塔尔科特·帕森斯 Talcott Parsons'''的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。后来，社会学家'''彼得·布劳 Peter Blau'''的'''社会交换论 Social Exchange Theory'''为分析社会单位之间的关系提供了强大的动力。

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref>{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref>Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader"/><ref>Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref>Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref>Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>

到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家'''哈里森·怀特 Harrison White'''和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的'''查尔斯·堤利 Charles Tilly'''，还有发表了六度分隔理论论文的'''斯坦利·米尔格拉姆 Stanley Milgram'''。'''马克·格兰诺维特 Mark Granovetter'''和'''巴里·威尔曼 Barry Wellman'''是怀特以前的学生，他们阐述并支持对社交网络的分析。

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如'''[[邓肯·瓦茨 Duncan J. Watts]]'''、'''[[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]]'''、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。

==Levels of analysis 分析层次==

[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]

Centrality中心性

In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref>Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref>{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref>{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref>{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].

一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。

===Micro level 微观层面===

At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.

在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。

'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].

二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。

'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.

三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。在'''弗里茨 · 海德 Fritz Heider'''的'''平衡理论 Balance Theory'''中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。

'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.

行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。

'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref>{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>

子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。

===Meso level 中观层面===

In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>

一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。

[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]

'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>

组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。

'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>

随机分布的网络: 社会网络的'''指数随机图模型 Exponential random graph models'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。

[[File:Scale-free network sample.png|thumb|right|图5：Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.

'''[[无标度网络]] A Scale-free Network''': 无标度网络网络是一个'''度分布 Degree Distribution'''遵循'''幂律 Power Law'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''枢纽 Hubs'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''集聚系数 General Characteristic'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。

===Macro level 宏观层面===

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].

宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。

[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]

'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).

大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。

'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>

复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''网络拓扑 Network Topology'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''同配性 Assortativity'''或非同配性、社区结构（见'''随机分块模型 Stochastic Block Model'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''三重显著性特征'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''随机图 Random Graph'''，并没有表现出这些特征。

==Theoretical links 理论联系==

=== Imported theories 输入的理论 ===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''平衡理论 Balance theory'''、社会比较论，以及最近的社会认同方法。

===Indigenous theories 本土理论===

Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].

很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>

异质性理论的基础是在一项研究中发现，更多的'''弱关系 Weak Tie'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。

==Structural holes 结构洞==

In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />

在网络的背景下，'''社会资本 Social Capital'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''结构洞 Structural Hole'''。因此，一个'''连接'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。

Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.

富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''商业社交 Business networking'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。

==Research clusters 研究集群==

===Communication 传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.

传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。

===Community 社区===

In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.

在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。

===Complex networks 复杂网络===

[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].

复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。

Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.

'''双相演化理论 Dual-phase Evolution'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。

===Criminal networks 犯罪网络===

In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

在'''犯罪学 Criminology和'''城市社会学 Urban sociology中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。

===Diffusion of innovations 创新扩散理论===

[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.

'''创新扩散理论 Diffusion of Innovations'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。

===Demography 人口学===

In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

在'''人口学 Demography'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。

===Economic sociology 经济社会学===

The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>

社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''经济社会学 Economic Sociology'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。

===Health care 卫生保健===

Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>

社会网络分析越来越多地被纳入卫生保健分析——不仅在'''流行病学 Epidemiology'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。

===Human ecology 人类生态学===

[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>

'''人类生态学 Human ecology'''是研究人类与其自然环境、社会环境和'''建成环境 Built environment'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。

===Language and linguistics 语言与语言学===

Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.

语言学和语言学的研究，特别是'''演化语言学 Evolutionary Linguistics'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。

===Literary networks 文学网络===

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.

在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。

===Organizational studies 组织研究===

Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>

研究正式或非正式组织关系、'''组织沟通 Organizational Communication'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''组织承诺 Organizational Commitment'''、组织认同、人际公民行为有影响。

===Social capital 社会资本===

[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>

社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。

[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>

社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。

=== Advertising 广告===

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===Network position and benefits 网络地位和利益===

In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

===Social media 社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.

计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在电脑中介传播（Computer-mediated Communication）环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着电子商务的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。

In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>

此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />

=='''参阅'''==

* [[Bibliography of sociology]] 社会学参考书目

* [[Business networking]] 商业社交

* [[Collective network]] 集体网络

* [[International Network for Social Network Analysis]] 国际社会网络学会

* [[Network society]] 网络社会

* [[Network theory]] 网络理论

*[[Network science]] 网络科学

* [[Semiotics of social networking]] 社交网络符号学

* [[Scientific collaboration network]] 科学合作网络

* [[Social network analysis]] 社会网络分析

* [[Social network (sociolinguistics)]] 社会网络

* [[Social networking service]] 社会网络服务

* [[Social web]] 社交网络

* [[Structural fold]] 结构折叠

==参考文献==

{{reflist}}

==拓展阅读==

* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}

* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}

* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}

* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}

* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}

* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}

* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}

* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}

* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}

* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}

* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}

* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}

==外部链接==

===组织===
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''

* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''

* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''

* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''

* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''

* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}

===课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg

* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle

* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti

* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)

===数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]

* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]

* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]

* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]

* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]

* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]

* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]

* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]

==编者推荐==
集智俱乐部：[https://swarma.org/?p=18646 社会网络分析的四个维度：研究方法、应用和软件工具综述 | 网络科学论文速递25篇]

[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。

{{DEFAULTSORT:Social Network}}

[[Category:Social networks| ]]

[[Category:Communication theory]]

Category:Communication theory

范畴: 传播理论

[[Category:Community building]]

Category:Community building

类别: 社区建设

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Network theory]]

Category:Network theory

范畴: 网络理论

[[Category:Organizational theory]]

Category:Organizational theory

类别: 组织行为学

[[Category:Self-organization]]

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类别: 自我组织

[[Category:Social information processing]]

Category:Social information processing

类别: 社会信息处理

[[Category:Social systems]]

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类别: 社会系统

[[Category:Sociological terminology]]

Category:Sociological terminology

类别: 社会学术语

[[Category:Sociological theories]]

Category:Sociological theories

范畴: 社会学理论

[[Category:Systems theory]]

Category:Systems theory

范畴: 系统论

<noinclude>

This page was moved from [[wikipedia:en:Social network]]. Its edit history can be viewed at [[社会网络/edithistory]]</noinclude>

[[Category:待整理页面]]

社会网络

2021-08-17T13:14:38Z

Kuangmy：全文整理翻译编辑

{{Network Science}}

A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.

社会网络（Social Network）是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用社会网络分析（Social Network Analysis）对这些结构进行研究，以确定本地及全球模式，定位有影响力的实体，并审查网络动态。

Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>

社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份社交关系图（Sociogram）以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />

==概览==

[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]

Barabási model.]]

The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].

社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于人类学 、生物学、传播学、经济学、地理学、信息科学、组织学、社会心理学、社会学和社会语言学。

==历史==

In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>

在19世纪90年代晚期，埃米尔·涂尔干（Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />

[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref>For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref>See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref>Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref>Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref>{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref>Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref>Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref>{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref>{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref>Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref>Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref>Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref>Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref>Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref>Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>

20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见'''社会测量 Sociometry）'''。在人类学中，社会网络理论的基础是'''布罗尼斯拉夫·马林诺夫斯基 Bronislaw Malinowski'''，'''阿尔弗雷德·拉德克利夫-布朗 Alfred Radcliffe-Brown'''和'''克洛德·列维-斯特劳斯 Claude Lévi-Strauss'''的理论和人种学著作。包括'''约翰·A·巴恩斯 John A. Barnes'''、'''J·克莱德·米切尔 J. Clyde Mitchell'''和'''伊丽莎白·博特·斯皮利厄斯 Elizabeth Bott Spillius'''在内的一群与'''马克斯·格拉克曼 Max Gluckman'''和'''曼彻斯特学派 Manchester School'''有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。在社会学方面，'''塔尔科特·帕森斯 Talcott Parsons'''的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。后来，社会学家'''彼得·布劳 Peter Blau'''的'''社会交换论 Social Exchange Theory'''为分析社会单位之间的关系提供了强大的动力。

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref>{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref>Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader"/><ref>Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref>Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref>Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>

到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家'''哈里森·怀特 Harrison White'''和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的'''查尔斯·堤利 Charles Tilly'''，还有发表了六度分隔理论论文的'''斯坦利·米尔格拉姆 Stanley Milgram'''。'''马克·格兰诺维特 Mark Granovetter'''和'''巴里·威尔曼 Barry Wellman'''是怀特以前的学生，他们阐述并支持对社交网络的分析。

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如'''[[邓肯·瓦茨 Duncan J. Watts]]'''、'''[[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]]'''、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。

==Levels of analysis 分析层次==

[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]

Centrality中心性

In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref>Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref>{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref>{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref>{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].

一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。

===Micro level 微观层面===

At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.

在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。

'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].

二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。

'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.

三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。在'''弗里茨 · 海德 Fritz Heider'''的'''平衡理论 Balance Theory'''中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。

'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.

行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。

'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref>{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>

子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。

===Meso level 中观层面===

In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>

一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。

[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]

'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>

组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。

'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>

随机分布的网络: 社会网络的'''指数随机图模型 Exponential random graph models'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。

[[File:Scale-free network sample.png|thumb|right|图5：Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.

'''[[无标度网络]] A Scale-free Network''': 无标度网络网络是一个'''度分布 Degree Distribution'''遵循'''幂律 Power Law'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''枢纽 Hubs'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''集聚系数 General Characteristic'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。

===Macro level 宏观层面===

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].

宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。

[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]

'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).

大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。

'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>

复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''网络拓扑 Network Topology'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''同配性 Assortativity'''或非同配性、社区结构（见'''随机分块模型 Stochastic Block Model'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''三重显著性特征'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''随机图 Random Graph'''，并没有表现出这些特征。

==Theoretical links 理论联系==

=== Imported theories 输入的理论 ===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''平衡理论 Balance theory'''、社会比较论，以及最近的社会认同方法。

===Indigenous theories 本土理论===

Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].

很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>

异质性理论的基础是在一项研究中发现，更多的'''弱关系 Weak Tie'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。

==Structural holes 结构洞==

In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />

在网络的背景下，'''社会资本 Social Capital'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''结构洞 Structural Hole'''。因此，一个'''连接'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。

Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.

富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''商业社交 Business networking'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。

==Research clusters 研究集群==

===Communication 传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.

传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。

===Community 社区===

In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.

在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。

===Complex networks 复杂网络===

[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].

复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。

Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.

'''双相演化理论 Dual-phase Evolution'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。

===Criminal networks 犯罪网络===

In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

在'''犯罪学 Criminology和'''城市社会学 Urban sociology中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。

===Diffusion of innovations 创新扩散理论===

[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.

'''创新扩散理论 Diffusion of Innovations'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。

===Demography 人口学===

In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

在'''人口学 Demography'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。

===Economic sociology 经济社会学===

The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>

社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''经济社会学 Economic Sociology'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。

===Health care 卫生保健===

Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>

社会网络分析越来越多地被纳入卫生保健分析——不仅在'''流行病学 Epidemiology'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。

===Human ecology 人类生态学===

[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>

'''人类生态学 Human ecology'''是研究人类与其自然环境、社会环境和'''建成环境 Built environment'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。

===Language and linguistics 语言与语言学===

Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.

语言学和语言学的研究，特别是'''演化语言学 Evolutionary Linguistics'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。

===Literary networks 文学网络===

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.

在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。

===Organizational studies 组织研究===

Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>

研究正式或非正式组织关系、'''组织沟通 Organizational Communication'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''组织承诺 Organizational Commitment'''、组织认同、人际公民行为有影响。

===Social capital 社会资本===

[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>

社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。

[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>

社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。

=== Advertising 广告===

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===Network position and benefits 网络地位和利益===

In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

===Social media 社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.

计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在电脑中介传播（Computer-mediated Communication）环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着电子商务的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。

In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>

此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />

=='''参阅'''==

* [[Bibliography of sociology]] 社会学参考书目

* [[Business networking]] 商业社交

* [[Collective network]] 集体网络

* [[International Network for Social Network Analysis]] 国际社会网络学会

* [[Network society]] 网络社会

* [[Network theory]] 网络理论

*[[Network science]] 网络科学

* [[Semiotics of social networking]] 社交网络符号学

* [[Scientific collaboration network]] 科学合作网络

* [[Social network analysis]] 社会网络分析

* [[Social network (sociolinguistics)]] 社会网络

* [[Social networking service]] 社会网络服务

* [[Social web]] 社交网络

* [[Structural fold]] 结构折叠

==参考文献==

{{reflist}}

==拓展阅读==

* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}

* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}

* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}

* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}

* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}

* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}

* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}

* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}

* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}

* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}

* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}

* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}

==外部链接==

===组织===
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''

* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''

* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''

* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''

* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''

* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}

===课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg

* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle

* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti

* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)

===数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]

* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]

* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]

* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]

* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]

* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]

* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]

* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]

==编者推荐==
集智俱乐部：[https://swarma.org/?p=18646 社会网络分析的四个维度：研究方法、应用和软件工具综述 | 网络科学论文速递25篇]

[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。

{{DEFAULTSORT:Social Network}}

[[Category:Social networks| ]]

[[Category:Communication theory]]

Category:Communication theory

范畴: 传播理论

[[Category:Community building]]

Category:Community building

类别: 社区建设

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Network theory]]

Category:Network theory

范畴: 网络理论

[[Category:Organizational theory]]

Category:Organizational theory

类别: 组织行为学

[[Category:Self-organization]]

Category:Self-organization

类别: 自我组织

[[Category:Social information processing]]

Category:Social information processing

类别: 社会信息处理

[[Category:Social systems]]

Category:Social systems

类别: 社会系统

[[Category:Sociological terminology]]

Category:Sociological terminology

类别: 社会学术语

[[Category:Sociological theories]]

Category:Sociological theories

范畴: 社会学理论

[[Category:Systems theory]]

Category:Systems theory

范畴: 系统论

<noinclude>

This page was moved from [[wikipedia:en:Social network]]. Its edit history can be viewed at [[社会网络/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-11T13:40:20Z

Kuangmy：/* Feature/aspect-based功能/属性为基础的情感分析 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.
* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name="Turney02" /> and Pang<ref name="PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name="PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name="SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name="PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name="SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name="Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name="KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
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| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497" />

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== '''Feature/aspect-based基于功能/属性的情感分析''' ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref name=":26">
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref name=":27">
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%<ref name=":26" /> 的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。<ref name=":27" />

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref name=":28">
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref name=":29">
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref name=":30">
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评分者非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在处理否定句、夸张句、笑话或讽刺句时会遇到困难，而这些句子对人类读者来说通常很容易处理，也就是说计算机系统所犯的一些错误在人类看来通常会显得过于幼稚。总的来说，学术研究中定义的情感分析在实际商业任务中的效用受到了质疑，主要是因为对于担心公众话语对品牌或企业声誉的影响的客户来说，从负面到正面的简单的单维度情感模型几乎没有提供什么可操作的信息。<ref name=":28" /><ref name=":29" /><ref name=":30" />

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref name=":31">
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref name=":32">
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情感分析的评估已转向更多基于任务的措施，这些措施是与公关机构和市场研究专业人士的代表共同制定的。例如，RepLab评估数据集中较少考虑的文本内容，而更多地关注文本对品牌声誉问题的影响。<ref name=":31" /><ref name=":32" /><ref name="replab2014" />

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评估越来越多地基于特定任务，每个分类器的都需要一个单独的训练模型来实现更准确地识别给定数据集的情感表达。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
参阅：声誉管理（Reputation management）、web 2.0和web数据挖掘（web mining）

The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref name=":33">{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情感分析的兴趣。随着评论、评级、推荐和其他形式的网络在线表达的激增，网络在线评论语料已经变成了一种虚拟货币，企业可以借此来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求将过滤噪音、理解对话、识别相关内容并采取适当行动的过程的自动化程度加深，许多企业将目光投向了情感分析领域。<ref name="Mining the Web for Feelings, Not Facts" /> 使问题进一步复杂化的是匿名社交媒体平台的崛起，如4chan和Reddit。<ref name=":33" />如果说web 2.0完全是关于民主化发布，那么web的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。<ref name="The Future of Social Media Monitoring" />

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

在研究中，朝着这个目标迈出了一步。目前，世界各地大学的几个研究团队正致力于通过情感分析来了解网络社区中的情感动态。<ref name="Collective emotions in cyberspace" /> 例如，CyberEmotions项目最近发现了负面情绪在推动社交网络讨论中的作用。<ref name="NewSci_flaming" />

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情感分析算法使用简单的术语来表达关于产品或服务的情感。然而，受到文化因素、语言上的细微差别以及不同的语境的影响，将文本字符串转换成简单的赞成或反对的情感变得极其困难。事实上，人类经常对文本的情感产生分歧，这一事实说明了计算机要做好这项工作是一项多么艰巨的任务。文本字符串越短，难度就越大。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref name=":34">Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale,<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，但对微型博客的情感分析已经表明，Twitter可以被视为一个有效的政治情感在线指标。Twitter的政治情感分析表显示它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。<ref name=":34" /> 此外，Twitter上的情感分析也被证明可以捕捉到，在全球范围内人类生殖周期背后的公众情感<ref name="r25" /> 以及其他与公共健康相关的问题（如药物不良反应）背后的公共情感<ref name="r27" />。

== Application in recommender systems 推荐系统中的应用 ==
{{See also|Recommender system}}
参阅：推荐系统（Recommender system）

For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

对于一个推荐系统来说，情感分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个项目的偏好。'''主流推荐系统是基于显性数据集工作的。例如，协同过滤（collaborative filtering）基于评分矩阵工作，基于内容的过滤（content-based filtering）基于项目元数据工作。'''

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref name=":35">{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以对商品提供文本评论、意见或反馈。这些用户生成的文本提供了丰富的用户对众多产品和商品的情感意见。对于一个商品而言，这样的文本可以同时显示商品的相关功能/属性以及用户对每个特性的看法。<ref name=":35" /> 在基于内容的过滤中，文本中描述的商品的功能/属性与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些特性，它们可以被视为能够显著影响用户对产品的体验的最关键的特性，而产品的元数据（通常由生产者而不是消费者提供）则可能忽略用户关心的特性。对于具有共同特征的不同商品，用户可能会有不同的情感意见。而且，同一个商品的不同特性也可能会得到不同用户不同的情感意见。用户对特征的情感可以看作是一个多维度的评分分值，它反映了用户对商品的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref name=":36">{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于功能/属性和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。<ref name=":0" /> 向用户推荐候选商品的动机有两种。第一种动力是候选商品与用户偏好商品具有许多共同特征，<ref name=":36" /> 第二种动机是候选商品在其特征上获得了高度的情感评价。对于一个偏好商品来说，有理由相信具有相同特性的商品将具有类似的功能或实用性。因此，这些商品也将有可能被用户所青睐。另一方面，对于两个候选商品的共同特征，其他用户可能给予其中一个正面的评价，而给予另一个负面的评价。显然，应该向用户推荐评价较高的商品。基于这两种动机，可以为每个候选商品建立相似度和情感评分的组合排序评分。<ref name=":0" />

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref name=":37">{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析还面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每条评论的有用性，<ref name=":37" />因为粗劣的评论或反馈对推荐系统几乎没有任何帮助。此外，评论可能被刻意设计成阻碍目标产品销售，因此即使它写得很好也会对推荐系统造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref name=":38">{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，应该用不同的方法处理用户生成的长文本和短文本。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，<ref name=":38" /> 因为它更容易过滤掉短形式文本中的干扰。对于长文本而言，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref name=":39">{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba和Madhusudhan<ref name=":39" /> 介绍了一种新的方法，即通过重新打包Twitter等社交媒体平台的情感分析结果，并以不同的形式提供基于时间的综合服务，来满足当今图书馆用户的信息需求。此外，他们还提出了一种利用社交媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also参阅==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情感
* 文体学

== References ==
{{Reflist|30em}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]
[[Category:待整理页面]]

情感分析

2021-08-11T09:05:03Z

Kuangmy：/* Evaluation 评估 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.
* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name="Turney02" /> and Pang<ref name="PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name="PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name="SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name="PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name="SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name="Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name="KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497" />

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== '''Feature/aspect-based功能/属性为基础的情感分析''' ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref name=":26">
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref name=":27">
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%<ref name=":26" /> 的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。<ref name=":27" />

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref name=":28">
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref name=":29">
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref name=":30">
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评分者非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在处理否定句、夸张句、笑话或讽刺句时会遇到困难，而这些句子对人类读者来说通常很容易处理，也就是说计算机系统所犯的一些错误在人类看来通常会显得过于幼稚。总的来说，学术研究中定义的情感分析在实际商业任务中的效用受到了质疑，主要是因为对于担心公众话语对品牌或企业声誉的影响的客户来说，从负面到正面的简单的单维度情感模型几乎没有提供什么可操作的信息。<ref name=":28" /><ref name=":29" /><ref name=":30" />

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref name=":31">
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref name=":32">
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情感分析的评估已转向更多基于任务的措施，这些措施是与公关机构和市场研究专业人士的代表共同制定的。例如，RepLab评估数据集中较少考虑的文本内容，而更多地关注文本对品牌声誉问题的影响。<ref name=":31" /><ref name=":32" /><ref name="replab2014" />

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评估越来越多地基于特定任务，每个分类器的都需要一个单独的训练模型来实现更准确地识别给定数据集的情感表达。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
参阅：声誉管理（Reputation management）、web 2.0和web数据挖掘（web mining）

The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref name=":33">{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情感分析的兴趣。随着评论、评级、推荐和其他形式的网络在线表达的激增，网络在线评论语料已经变成了一种虚拟货币，企业可以借此来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求将过滤噪音、理解对话、识别相关内容并采取适当行动的过程的自动化程度加深，许多企业将目光投向了情感分析领域。<ref name="Mining the Web for Feelings, Not Facts" /> 使问题进一步复杂化的是匿名社交媒体平台的崛起，如4chan和Reddit。<ref name=":33" />如果说web 2.0完全是关于民主化发布，那么web的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。<ref name="The Future of Social Media Monitoring" />

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

在研究中，朝着这个目标迈出了一步。目前，世界各地大学的几个研究团队正致力于通过情感分析来了解网络社区中的情感动态。<ref name="Collective emotions in cyberspace" /> 例如，CyberEmotions项目最近发现了负面情绪在推动社交网络讨论中的作用。<ref name="NewSci_flaming" />

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情感分析算法使用简单的术语来表达关于产品或服务的情感。然而，受到文化因素、语言上的细微差别以及不同的语境的影响，将文本字符串转换成简单的赞成或反对的情感变得极其困难。事实上，人类经常对文本的情感产生分歧，这一事实说明了计算机要做好这项工作是一项多么艰巨的任务。文本字符串越短，难度就越大。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref name=":34">Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale,<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，但对微型博客的情感分析已经表明，Twitter可以被视为一个有效的政治情感在线指标。Twitter的政治情感分析表显示它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。<ref name=":34" /> 此外，Twitter上的情感分析也被证明可以捕捉到，在全球范围内人类生殖周期背后的公众情感<ref name="r25" /> 以及其他与公共健康相关的问题（如药物不良反应）背后的公共情感<ref name="r27" />。

== Application in recommender systems 推荐系统中的应用 ==
{{See also|Recommender system}}
参阅：推荐系统（Recommender system）

For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

对于一个推荐系统来说，情感分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个项目的偏好。'''主流推荐系统是基于显性数据集工作的。例如，协同过滤（collaborative filtering）基于评分矩阵工作，基于内容的过滤（content-based filtering）基于项目元数据工作。'''

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref name=":35">{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以对商品提供文本评论、意见或反馈。这些用户生成的文本提供了丰富的用户对众多产品和商品的情感意见。对于一个商品而言，这样的文本可以同时显示商品的相关功能/属性以及用户对每个特性的看法。<ref name=":35" /> 在基于内容的过滤中，文本中描述的商品的功能/属性与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些特性，它们可以被视为能够显著影响用户对产品的体验的最关键的特性，而产品的元数据（通常由生产者而不是消费者提供）则可能忽略用户关心的特性。对于具有共同特征的不同商品，用户可能会有不同的情感意见。而且，同一个商品的不同特性也可能会得到不同用户不同的情感意见。用户对特征的情感可以看作是一个多维度的评分分值，它反映了用户对商品的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref name=":36">{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于功能/属性和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。<ref name=":0" /> 向用户推荐候选商品的动机有两种。第一种动力是候选商品与用户偏好商品具有许多共同特征，<ref name=":36" /> 第二种动机是候选商品在其特征上获得了高度的情感评价。对于一个偏好商品来说，有理由相信具有相同特性的商品将具有类似的功能或实用性。因此，这些商品也将有可能被用户所青睐。另一方面，对于两个候选商品的共同特征，其他用户可能给予其中一个正面的评价，而给予另一个负面的评价。显然，应该向用户推荐评价较高的商品。基于这两种动机，可以为每个候选商品建立相似度和情感评分的组合排序评分。<ref name=":0" />

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref name=":37">{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析还面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每条评论的有用性，<ref name=":37" />因为粗劣的评论或反馈对推荐系统几乎没有任何帮助。此外，评论可能被刻意设计成阻碍目标产品销售，因此即使它写得很好也会对推荐系统造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref name=":38">{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，应该用不同的方法处理用户生成的长文本和短文本。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，<ref name=":38" /> 因为它更容易过滤掉短形式文本中的干扰。对于长文本而言，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref name=":39">{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba和Madhusudhan<ref name=":39" /> 介绍了一种新的方法，即通过重新打包Twitter等社交媒体平台的情感分析结果，并以不同的形式提供基于时间的综合服务，来满足当今图书馆用户的信息需求。此外，他们还提出了一种利用社交媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also参阅==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情感
* 文体学

== References ==
{{Reflist|30em}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]
[[Category:待整理页面]]

情感分析

2021-08-11T09:01:54Z

Kuangmy：/* Web 2.0 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
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}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== '''Feature/aspect-based功能/属性为基础的情感分析''' ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref name=":26">
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref name=":27">
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%<ref name=":26" /> 的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。<ref name=":27" />

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref name=":28">
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref name=":29">
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref name=":30">
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评分者非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在处理否定句、夸张句、笑话或讽刺句时会遇到困难，而这些句子对人类读者来说通常很容易处理，也就是说计算机系统所犯的一些错误在人类看来通常会显得过于幼稚。总的来说，学术研究中定义的情感分析在实际商业任务中的效用受到了质疑，主要是因为对于担心公众话语对品牌或企业声誉的影响的客户来说，从负面到正面的简单的单维度情感模型几乎没有提供什么可操作的信息。<ref name=":28" /><ref name=":29" /><ref name=":30" />

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref name=":31">
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref name=":32">
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情感分析的评估已转向更多基于任务的措施，这些措施是与公关机构和市场研究专业人士的代表共同制定的。例如，RepLab评估数据集中较少考虑的文本内容，而更多地关注文本对品牌声誉问题的影响。<ref name=":31" /><ref name=":32" /><ref name="replab2014" />

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评估越来越多地基于特定任务，每个分类器的都需要一个单独的训练模型来实现更准确地识别给定数据集的情感表达。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
参阅：声誉管理（Reputation management）、web 2.0和web数据挖掘（web mining）

The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref name=":33">{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情感分析的兴趣。随着评论、评级、推荐和其他形式的网络在线表达的激增，网络在线评论语料已经变成了一种虚拟货币，企业可以借此来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求将过滤噪音、理解对话、识别相关内容并采取适当行动的过程的自动化程度加深，许多企业将目光投向了情感分析领域。<ref name="Mining the Web for Feelings, Not Facts" /> 使问题进一步复杂化的是匿名社交媒体平台的崛起，如4chan和Reddit。<ref name=":33" />如果说web 2.0完全是关于民主化发布，那么web的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。<ref name="The Future of Social Media Monitoring" />

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

在研究中，朝着这个目标迈出了一步。目前，世界各地大学的几个研究团队正致力于通过情感分析来了解网络社区中的情感动态。<ref name="Collective emotions in cyberspace" /> 例如，CyberEmotions项目最近发现了负面情绪在推动社交网络讨论中的作用。<ref name="NewSci_flaming" />

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情感分析算法使用简单的术语来表达关于产品或服务的情感。然而，受到文化因素、语言上的细微差别以及不同的语境的影响，将文本字符串转换成简单的赞成或反对的情感变得极其困难。事实上，人类经常对文本的情感产生分歧，这一事实说明了计算机要做好这项工作是一项多么艰巨的任务。文本字符串越短，难度就越大。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref name=":34">Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale,<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，但对微型博客的情感分析已经表明，Twitter可以被视为一个有效的政治情感在线指标。Twitter的政治情感分析表显示它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。<ref name=":34" /> 此外，Twitter上的情感分析也被证明可以捕捉到，在全球范围内人类生殖周期背后的公众情感<ref name="r25" /> 以及其他与公共健康相关的问题（如药物不良反应）背后的公共情感<ref name="r27" />。

== Application in recommender systems 推荐系统中的应用 ==
{{See also|Recommender system}}
参阅：推荐系统（Recommender system）

For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

对于一个推荐系统来说，情感分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个项目的偏好。'''主流推荐系统是基于显性数据集工作的。例如，协同过滤（collaborative filtering）基于评分矩阵工作，基于内容的过滤（content-based filtering）基于项目元数据工作。'''

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref name=":35">{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以对商品提供文本评论、意见或反馈。这些用户生成的文本提供了丰富的用户对众多产品和商品的情感意见。对于一个商品而言，这样的文本可以同时显示商品的相关功能/属性以及用户对每个特性的看法。<ref name=":35" /> 在基于内容的过滤中，文本中描述的商品的功能/属性与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些特性，它们可以被视为能够显著影响用户对产品的体验的最关键的特性，而产品的元数据（通常由生产者而不是消费者提供）则可能忽略用户关心的特性。对于具有共同特征的不同商品，用户可能会有不同的情感意见。而且，同一个商品的不同特性也可能会得到不同用户不同的情感意见。用户对特征的情感可以看作是一个多维度的评分分值，它反映了用户对商品的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref name=":36">{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于功能/属性和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。<ref name=":0" /> 向用户推荐候选商品的动机有两种。第一种动力是候选商品与用户偏好商品具有许多共同特征，<ref name=":36" /> 第二种动机是候选商品在其特征上获得了高度的情感评价。对于一个偏好商品来说，有理由相信具有相同特性的商品将具有类似的功能或实用性。因此，这些商品也将有可能被用户所青睐。另一方面，对于两个候选商品的共同特征，其他用户可能给予其中一个正面的评价，而给予另一个负面的评价。显然，应该向用户推荐评价较高的商品。基于这两种动机，可以为每个候选商品建立相似度和情感评分的组合排序评分。<ref name=":0" />

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref name=":37">{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析还面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每条评论的有用性，<ref name=":37" />因为粗劣的评论或反馈对推荐系统几乎没有任何帮助。此外，评论可能被刻意设计成阻碍目标产品销售，因此即使它写得很好也会对推荐系统造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref name=":38">{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，应该用不同的方法处理用户生成的长文本和短文本。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，<ref name=":38" /> 因为它更容易过滤掉短形式文本中的干扰。对于长文本而言，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref name=":39">{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba和Madhusudhan<ref name=":39" /> 介绍了一种新的方法，即通过重新打包Twitter等社交媒体平台的情感分析结果，并以不同的形式提供基于时间的综合服务，来满足当今图书馆用户的信息需求。此外，他们还提出了一种利用社交媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also参阅==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情感
* 文体学

== References ==
{{Reflist|30em}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]
[[Category:待整理页面]]

情感分析

2021-08-11T06:42:37Z

Kuangmy：/* Methods and features方法和特征 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref name=":26">
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref name=":27">
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%<ref name=":26" /> 的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。<ref name=":27" />

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref name=":28">
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref name=":29">
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref name=":30">
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评分者非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在处理否定句、夸张句、笑话或讽刺句时会遇到困难，而这些句子对人类读者来说通常很容易处理，也就是说计算机系统所犯的一些错误在人类看来通常会显得过于幼稚。总的来说，学术研究中定义的情感分析在实际商业任务中的效用受到了质疑，主要是因为对于担心公众话语对品牌或企业声誉的影响的客户来说，从负面到正面的简单的单维度情感模型几乎没有提供什么可操作的信息。<ref name=":28" /><ref name=":29" /><ref name=":30" />

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref name=":31">
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref name=":32">
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情感分析的评估已转向更多基于任务的措施，这些措施是与公关机构和市场研究专业人士的代表共同制定的。例如，RepLab评估数据集中较少考虑的文本内容，而更多地关注文本对品牌声誉问题的影响。<ref name=":31" /><ref name=":32" /><ref name="replab2014" />

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评估越来越多地基于特定任务，每个分类器的都需要一个单独的训练模型来实现更准确地识别给定数据集的情感表达。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
参阅：

The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T13:44:30Z

Kuangmy：/* Evaluation 评估 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref name=":26">
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref name=":27">
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%<ref name=":26" /> 的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。<ref name=":27" />

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref name=":28">
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref name=":29">
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref name=":30">
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评分者非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在处理否定句、夸张句、笑话或讽刺句时会遇到困难，而这些句子对人类读者来说通常很容易处理，也就是说计算机系统所犯的一些错误在人类看来通常会显得过于幼稚。总的来说，学术研究中定义的情感分析在实际商业任务中的效用受到了质疑，主要是因为对于担心公众话语对品牌或企业声誉的影响的客户来说，从负面到正面的简单的单维度情感模型几乎没有提供什么可操作的信息。<ref name=":28" /><ref name=":29" /><ref name=":30" />

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref name=":31">
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref name=":32">
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情感分析的评估已转向更多基于任务的措施，这些措施是与公关机构和市场研究专业人士的代表共同制定的。例如，RepLab评估数据集中较少考虑的文本内容，而更多地关注文本对品牌声誉问题的影响。<ref name=":31" /><ref name=":32" /><ref name="replab2014" />

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评估越来越多地基于特定任务，每个分类器的都需要一个单独的训练模型来实现更准确地识别给定数据集的情感表达。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T13:24:48Z

Kuangmy：/* Evaluation 评估 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，并认为自动化情感分类器最终可以达到的与人类一致的判断上限。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

原则上来说，情感分析系统的准确性就是它与人类判断的一致性程度。这通常由基于负面和正面文本这两个目标类别识别的查准率和查全率的变量来衡量的。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据现有研究，人类评分员之间通常只有80%的几率是达成一致的（参见评分者之间的信度Inter-rater reliability）。因此，一个情感分类的程序如果能够达到70%的准确率，那么尽管这样的准确率这听起来还不算引人注目，但它的表现已经和人工识别的表现得几乎一样好。同时需要注意的是，因为人类本身对任何情感分类的答案都可能有很大的不同意见，如果一个程序有100%的准确率，人类仍然会有20%的可能不同意其判断的结果。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

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This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

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情感分析

2021-08-09T12:44:15Z

Kuangmy：/* Methods and features方法和特征 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref name=":24">{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name="Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref name=":25">{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

有很多开源软件工具以及一系列免费和付费的情感分析工具利用机器学习、统计学方法和自然语言处理的技术，对大型文本语料进行情感分析, 这些大型文本语料包括网页、网络新闻、互联网在线讨论群组、网络在线评论、网络博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010" /> 另一方面，基于知识的系统利用公开可用的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。<ref name=":24" /> 此外，情感分析也可以在视觉内容层面上进行，例如多模态情感分析（multimodal sentiment analysis）中对图像和视频进行分析。这方面的第一种方法是SentiBank。<ref name="Borth13" /> SentiBank方法利用形容词-名词对来代表视觉内容的属性。另外，绝大多数的情感分类方法都依赖于词袋模型（bag-of-words model），它忽略上下文语境、语法甚至是语序。根据词语如何构成较长短语的意义来分析情感的方法显示出了更好的效果，<ref name=":25" /> 但它们会也会导致产生额外的标识成本。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中，需要有人工分析的成分。因为自动化系统无法分析评论者个人的历史倾向，也无法分析平台的历史倾向，这往往导致对表达的情感的错误分类。自动化情感分类器通常能够识别大约23% 被人类正确分类的评论。然而，人们往往不同意这种说法，。并认为人与人之间的一致意见提供了一个自动化情感分类器最终可以达到的上限

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T09:38:23Z

Kuangmy：/* Methods and features方法和特征 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
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| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析的方法主要可以分成三类：基于知识的技术（knowledge-based techniques）、统计方法（statistical methods）和混合方法（hybrid approaches）。<ref name="“Cambria" /> 基于知识的技术根据明确的情感词（如快乐、悲伤、害怕和无聊）的存在对文本进行分类。<ref name="Ortony" /> 一些知识库不仅列出了明显的情感，而且还赋予了任意词汇与特定情感可能的“亲和性”。<ref name="Stevenson" /> 统计方法通过调控机器学习中的元素，比如潜在语意分析（latent semantic analysis），SVM（support vector machines），词袋（bag of words），（Pointwise Mutual Information for Semantic Orientation）和深度学习（depp learning）等等。一些复杂的方法意在检测出情感持有者（比如，保持情绪状态的那个人）和情感目标（比如，让情感持有者产生情绪的实体）。<ref name="Kim+Hovy06" /> 语法依赖关系是通过对文本的深度解析得到的。<ref name="DeyHaque08" /> 与单纯的语义技术不同的是，混合算法的思路利用了知识表达（knowledge representation）的元素，比如知识本体（ontologies）、语意网络（semantic networks），因此这种算法也可以检测到文字间比较微妙的情感表达。例如，通过分析一些没有明确表达相关信息的概念与明确概念的隐性的联系来获取所求信息。<ref name="“Hussain" />要想挖掘在某语境下的意见，或是获取被给予意见的某项功能，需要使用到语法之间的关系。语法之间互相的关联性经常需要通过深度解析文本来获取。'''【翻译到这里】'''

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T09:17:26Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的标记数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要人工手动生成一个大体量的带标记的句子数据集。与自动学习相比，人工标记的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref name=":17">{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的差异。在人工标记过程中，标记者之间会受限于语言的模糊性，从而可能出现对例子是主观还是客观的判断分歧。
# 人为错误。人工标记是一项细致的工作，需要精力高度集中才能完成。
# 耗时长。人工注释是一项繁重的工作。Riloff(1996)的调查研究表明，一个标记者完成160篇文本标记需要8个小时。<ref name=":17" />
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

上面所有提到的这些原因都会影响主客观分类的效率和效果。因此，研究者设计了两种自举算法（bootstrapping methods），这两种方法的目的是从未标记的文本数据中学习语言模式。两种方法都以少量种子词和大量未标记的文本语料开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref name=":18">{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref name=":19">{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# Meta-Bootstrapping（Riloff & Jones，1999）。<ref name=":18" /> 第一步: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 将分数排名前5的单词标记并添加到语义字典中。重复上述方法。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) （Thelen & Riloff，2002）。<ref name=":19" /> 第一步: 生成抽取模式；第二步: 将最好的模式从模式池移动到候选种子词池。第三步: 将分数排名前10的单词标记并添加到语义字典中。重复上述方法。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总体而言，这些算法突出了主观性和客观性识别任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref name=":20">{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观和客观分类器可以增强自然语言处理的服务应用。该分类器的主要好处之一是，它使数据驱动的决策过程在各个行业中得到普及。据Liu介绍，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实践。<ref name=":20" />

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref name=":21">{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref name=":22">{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref name=":23">{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类：在商业行业，分类器帮助公司更好地理解产品的反馈和对评论背后逻辑的推理。
* 股票价格预测：在金融行业，分类器通过处理从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。过去Dong等对日本股票价格的研究表明，带有主观和客观模块的模型可能比没有主客观模块的模型表现更好。<ref name=":21" />
* 社交媒体分析。
* 学生意见分类。<ref name=":22" />
* 篇章总结: 分类器可以提取目标制定的评论，并收集一个特定实体的意见。
* 复杂问题回答：分类器可以对复杂的问题进行分类，包括语言主体、目标和重点目标。在Yu等(2003)的研究中，研究人员开发了一个句子和篇章级别的聚类用来识别意见块。<ref name=":23" />
* 特定领域的应用。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T08:33:01Z

Kuangmy：

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
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| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的注释数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要手动生成一个大体量的带注释的句子数据集。与自动学习相比，手动注释的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。
# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。
# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T08:29:43Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的注释数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要手动生成一个大体量的带注释的句子数据集。与自动学习相比，手动注释的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。
# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。
# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T07:50:55Z

Kuangmy：正极性-->正面etc

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正面的、负面的还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负面的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正面和负面的情感二分类对其他内容进行评估。二是，一步构建包含中性、正面和负面三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正面和负面的语言，那么过滤掉中性语言并关注正面和负面情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正面和负面影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
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| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负面、中性和正面相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正面和负面的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(正面、负面、中性) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)正面的观点，2)中性的观点，3)负面的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。
# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。
# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-09T07:46:31Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负极性、中性和正极性相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正极性和负极性的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(积极的，消极的，中性的) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)积极的观点，2)中性的观点，3)消极的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含隐喻性的表达可能会影响抽取的表现。<ref name=":13" /> 此外，隐喻可能采取不同的形式，这会增加识别的难度。
# 写作中的差异：对于从互联网上获得的文本，目标文本数据的写作差异涉及不同的写作类型和风格。
# 上下文敏感性：根据前后句的主观性或客观性，分类会有所不同。<ref name=":1" />
# 时间敏感性：该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要确认新闻中的事实，他们需要比新闻变得过时的更长的时间进行交叉验证。
# 代表性词用法较少：关键提示词使用的次数很少。
# 不断增长的数量：这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。
# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。
# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。
# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based功能/属性为基础的情感分析 ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref name=":14">{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref name=":15">{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref name=":16">{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>一个更加优化的分析模型叫做“功能/属性为基础的情感分析（feature/aspect-based sentiment analysis）”。这是指判定针对一个实体在某一个方面或者某一功能下表现出来的意见或是情感，实体可能是一个手机、一个数码相机或者是一个银行<ref name="HuLiu04" /> 。“功能”或者“属性”是一件实体的某个属性或者组成部分，例如手机的屏幕、参观的服务或者是相机的图像质量等。不同的特征会产生不同的情感反应，比如一个酒店可能有方便的位置，但食物却很普通。<ref name=":14" /> 这个问题涉及到若干个子问题，譬如，识别相关的实体，提取它们的功能或属性，然后判断对每个特征/方面表达的意见是正面的、负面的还是中性的。<ref name="LiuHuCheng04" /> 特征的自动识别可以通过语法方法、主题建模<ref name=":15" /><ref name=":16" /> 或深度学习来实现。<ref name="Poria" /><ref name="Ma" /> 更多关于这个层面的情感分析的讨论可以参照NLP手册“情感分析和主观性（Sentiment Analysis and Subjectivity）”这一章。<ref name="Liu2010" />

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.有很多开源软件使用机器学习（machine learning）、统计、自然语言处理的技术来计算大型文本集的情感分析, 这些大型文本集合包括网页、网络新闻、网上讨论群、网络评论、博客和社交媒介。<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-06T12:56:39Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
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| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
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| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
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| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负极性、中性和正极性相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正极性和负极性的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(积极的，消极的，中性的) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)积极的观点，2)中性的观点，3)消极的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)代表性词用法较少，5)时间敏感性，以及6)不断增长的数量。【翻译到这里】

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref>{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含的隐喻表达可能会影响抽取的性能。此外，隐喻采取不同的形式，这可能有助于增加检测。# 文字上的差异。对于从互联网上获得的文本，目标文本数据的写作风格差异涉及不同的写作类型和风格 # 上下文敏感。分类可以根据前面和后面句子的主观性或客观性而有所不同。# 时间敏感属性。该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要在新闻中证实一个事实，他们需要更长的时间，比新闻变得过时更长的交叉验证。# 暗示用词较少的词语。# 不断增长的数量。这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。
# 写作中的差异
# 上下文敏感性
# 代表性词用法较少
# 时间敏感性
# 不断增长的数量

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref>{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref>{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref>{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-06T12:42:57Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负极性、中性和正极性相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正极性和负极性的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(积极的，消极的，中性的) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)积极的观点，2)中性的观点，3)消极的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref name=":11">{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref name=":12">{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类别的单词或短语指标集合都是为了在未注释的文本上找到理想的模式而定义的。对于主观表达，已经建立了一个不同的单词列表。Riloff等人(2003)指出，语言学家和自然语言处理领域的多位研究人员已经开发出了单词或短语的主观指标列表。<ref name=":11" /> 必须为测量给定的表达方式创建一个提取规则的字典是非常必要的。多年来，在主观性识别方面，从1999年的手工特征提取发展到了2005年的自动特征学习。<ref name=":12" />目前，自动学习方法可以进一步分为监督学习和无监督学习。利用机器学习对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达方式制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)代表性词用法较少，5)时间敏感性，以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref>{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 隐喻性的表达：文本中包含的隐喻表达可能会影响抽取的性能。此外，隐喻采取不同的形式，这可能有助于增加检测。# 文字上的差异。对于从互联网上获得的文本，目标文本数据的写作风格差异涉及不同的写作类型和风格 # 上下文敏感。分类可以根据前面和后面句子的主观性或客观性而有所不同。# 时间敏感属性。该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要在新闻中证实一个事实，他们需要更长的时间，比新闻变得过时更长的交叉验证。# 暗示用词较少的词语。# 不断增长的数量。这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。
# 写作中的差异
# 上下文敏感性
# 代表性词用法较少
# 时间敏感性
# 不断增长的数量

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref>{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref>{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref>{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-06T12:29:21Z

Kuangmy：/* Subjectivity/objectivity identification 主观性/客观性识别 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
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}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负极性、中性和正极性相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正极性和负极性的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(积极的，消极的，中性的) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)积极的观点，2)中性的观点，3)消极的观点。<ref name="Liu2010" />

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref>{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类的单词或短语指示符的集合被定义用于在未注释的文本上定位理想的模式。对于主观表达，已经创建了一个不同的词表。单词或短语中的主观指标列表是由 Riloff 语言学家和自然语言处理领域的多名研究人员开发的。必须创建一个抽取规则字典来度量给定的表达式。多年来，在主观检测方面，从1999年的手工特征提取到2005年的自动特征学习。目前，自动化学习方法可以进一步分为监督学习和非监督式学习学习。利用机器学习过程对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到，在为表达式制定一套固定的规则方面存在一些挑战。规则开发中的许多挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战: 1)隐喻性的表达方式，2)写作中的差异，3)上下文敏感性，4)代表用法较少的单词，5)时间敏感性，以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref>{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 比喻性的表达。文本中包含的隐喻表达可能会影响抽取的性能。此外，隐喻采取不同的形式，这可能有助于增加检测。# 文字上的差异。对于从互联网上获得的文本，目标文本数据的写作风格差异涉及不同的写作类型和风格 # 上下文敏感。分类可以根据前面和后面句子的主观性或客观性而有所不同。# 时间敏感属性。该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要在新闻中证实一个事实，他们需要更长的时间，比新闻变得过时更长的交叉验证。# 暗示用词较少的词语。# 不断增长的数量。这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref>{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref>{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref>{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-06T12:10:15Z

Kuangmy：

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref name=":7">{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref name=":8">{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref name=":9">{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref>This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种不同的识别情感的方法是使用一个量表系统，在这个系统中负极性、中性和正极性相关的词语被赋予了-10到+10的取值，代表着从最负面到最正面，或者是简单地从0到正面的上限，如+4。这使得我们能够根据环境（通常是在句子语境的层次上）调整特定语言的情感极性程度。当使用自然语言处理对一段非结构化文本进行分析时，基于情感词与概念的关联方式及其相关分数，对指定环境中的每个概念进行评分。<ref name=":7" /><ref name=":8" /><ref name=":9" /> 。这使得人们可以对情感有更深入的理解，因为现在依据相周围可能发生的变化调整一个概念的情感程度，例如，强化、缓和或否定概念所表达的情感的词语会影响它的得分。或者，如果目的是确定文本中的情感而不是文本的整体极性和强度，则可以给文本一个正极性和负极性的情感强度得分。<ref name="SentiStrength2010" />

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如功能/属性为基础的情感分析、分级情感分析(积极的，消极的，中性的) 、多语言情感分析和情感识别。

=== Subjectivity/objectivity identification 主观性/客观性识别 ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这一任务被普遍地定义为将给定的文本识别为主观和客观两个类别。<ref name="PangLee08Subjectivity" /> 这个问题有时甚至比极性分类更加难以解决。<ref name="MihalceaAl07" /> 词或短语的主观性取决于特定的上下文语境，客观的篇章有时候又包含了主观的句子（比如，一篇新闻中引用了其他人的观点）。此外，正如Su<ref name="SuMarkert08" /> 所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，Pang<ref name="PangLee04" /> 的研究表明，在对篇章文本进行极性分类之前去掉文本中的客观句子有助于提高模型的表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观这个术语指的是携带事实信息的事件。

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子:”要当选美国总统，候选人必须年满35岁。'

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions.Also known as 'private states' mentioned by Quirk et al.<ref>{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被称为私有状态。在下面的例子中，它反映了一个私人国家“我们美国人”。此外，被评论的目标实体可以采取从有形产品到刘(2010)所述无形话题事项的多种形式。此外，刘(2010)观察到三种态度: 1)积极的观点，2)中立的观点，3)消极的观点。

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子: 我们美国人需要选出一位成熟且能够做出明智决定的总统。'这种分析是一个分类问题。
Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref>{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类的单词或短语指示符的集合被定义用于在未注释的文本上定位理想的模式。对于主观表达，已经创建了一个不同的词表。单词或短语中的主观指标列表是由 Riloff 语言学家和自然语言处理领域的多名研究人员开发的。必须创建一个抽取规则字典来度量给定的表达式。多年来，在主观检测方面，从1999年的手工特征提取到2005年的自动特征学习。目前，自动化学习方法可以进一步分为监督学习和非监督式学习学习。利用机器学习过程对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到，在为表达式制定一套固定的规则方面存在一些挑战。规则开发中的许多挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战: 1)隐喻性的表达方式，2)写作中的差异，3)上下文敏感性，4)代表用法较少的单词，5)时间敏感性，以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref>{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 比喻性的表达。文本中包含的隐喻表达可能会影响抽取的性能。此外，隐喻采取不同的形式，这可能有助于增加检测。# 文字上的差异。对于从互联网上获得的文本，目标文本数据的写作风格差异涉及不同的写作类型和风格 # 上下文敏感。分类可以根据前面和后面句子的主观性或客观性而有所不同。# 时间敏感属性。该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要在新闻中证实一个事实，他们需要更长的时间，比新闻变得过时更长的交叉验证。# 暗示用词较少的词语。# 不断增长的数量。这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref>{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref>{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref>{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
|last3 = Collins
|title = The Cognitive Structure of Emotions
|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

<noinclude>

This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

[[Category:待整理页面]]

情感分析

2021-08-06T11:28:11Z

Kuangmy：/* Types类型 */

此词条暂由彩云小译翻译，翻译字数共4529，未经人工整理和审校，带来阅读不便，请见谅。

{{short description|marketing and customer service tool}}

'''Sentiment analysis''' (also known as '''opinion mining''' or '''emotion AI''') is the use of [[natural language processing]], [[Text analytics|text analysis]], [[computational linguistics]], and [[biometrics]] to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to [[voice of the customer]] materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from [[marketing]] to [[Customer relationship management|customer service]] to clinical medicine.

文本情感分析（也称为意见挖掘）是指用自然语言处理、文本挖掘以及计算机语言学等方法来识别、提取、量化和研究原素材中的情感状态和主观信息。情感分析被广泛应用于来源于用户的素材，如评论和调查回复，在线和社交媒体；也被应用于来源于卫生保健的素材，其应用范围涵盖了从市场营销到客户服务到临床医学的各个方面。

== Examples 案例 ==
The objective and challenges of sentiment analysis can be shown through some simple examples.

情感分析的目的和挑战可以通过一些简单的例子来说明。

=== Simple cases 简单例子 ===

* Coronet has the best lines of all day cruisers.
* Bertram has a deep V hull and runs easily through seas.
* Pastel-colored 1980s day cruisers from Florida are ugly.
* I dislike old [[cabin cruiser]]s.

* Coronet 拥有全天式游艇中最好的航线。
* Bertram有一个深v型的船身，可以轻松通过海洋。
* 来自佛罗里达州的20世纪80年代的粉彩全天式游艇很丑。
* 我不喜欢旧的游艇。

=== More challenging examples更具挑战性的例子 ===

* I do not dislike cabin cruisers. ([[Negation]] handling)
* Disliking watercraft is not really my thing. (Negation, inverted [[word order]])
* Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]. ([[Adverbial]] modifies the sentiment)
* I'd really truly love going out in this weather! (Possibly [[sarcastic]])
* Chris Craft is better looking than Limestone. (Two [[brand name]]s, identifying the target of attitude is difficult).
* Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names).
* The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains).
* You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
* I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)

* 我不是不喜欢游艇（I do not dislike cabin cruisers）。(否定处理)
* 不喜欢船不是我真正的爱好（Disliking watercraft is not really my thing）。(否定，倒置的词序)
* 有时候我真的很讨厌肋骨（Sometimes I really hate [[Rigid-hulled inflatable boat|RIBs]]）。(状语修饰感情)
* 我真的真的很喜欢在这种天气出去（I'd really truly love going out in this weather）！(可能是讽刺)
* Chris Craft比Limestone好看（Chris Craft is better looking than Limestone）。(两个品牌，识别目标的态度是困难的)。
* Chris Craft比Limestone好看，但的适航性和可靠性更突出（Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability）。(两种态度，两个品牌)。
* 这部电影有很多令人不安的情节，非常令人感到惊奇（The movie is surprising with plenty of unsettling plot twists）。(在某些领域中贬义褒用)。
* 你应该看看他们的甜点菜单（You should see their decadent dessert menu）。(最近某些态度术语的极性在一些领域中发生了改变)
* 我喜欢自己的手机，但不会向任何同事推荐（I love my mobile but would not recommend it to any of my colleagues）。(有保留的积极情绪，很难归类）

== Types类型 ==

A basic task in sentiment analysis is classifying the ''polarity'' of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. <ref name=":2"> Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. "Emotion Recognition
for Vietnamese Social Media Text". In Proceedings of the 2019 International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), Hanoi, Vietnam (2019).</ref>

情感分析的最底层的任务是识别给定的情感评论文本中的极性倾向是正极性、负极性还是中性的。按照处理文本的粒度不同，情感分析可以分为篇章级、句子级和词语级三个研究层次。高级的“超极性”情感分类研究关注有如情绪状态等，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

Precursors to sentimental analysis include the General Inquirer,<ref name=":3">Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).</ref> which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's [[psychological state]] based on analysis of their verbal behavior.<ref name=":4">Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.</ref>

情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />

Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| number = 7,136,877
| status = Issued
| title = System and method for determining and controlling the impact of text
| pubdate = June 28, 2001
| gdate =
| fdate =
| pridate =
| inventor = Volcani, Yanon;
| invent1 =
| invent2 = Fogel, David B.
| assign1 =
| assign2 =
| class =
| url = http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=2&p=1&f=G&l=50&d=PTXT&S1=(fogel.INNM.+AND+volcani.INNM.)&OS=in/fogel+and+in/volcani&RS=(IN/fogel+AND+IN/volcani)
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

随后，Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee | author2-link = Lillian Lee (computer scientist)
| first3 = Shivakumar | last3 = Vaithyanathan
| title = Thumbs up? Sentiment Classification using Machine Learning Techniques
| book-title = Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
| year = 2002
| pages = 79–86
| url = http://www.cs.cornell.edu/home/llee/papers/sentiment.home.html
}}
</ref> who applied different methods for detecting the polarity of [[product review]]s and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang<ref name = "PangLee05">
{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2005
| pages = 115–124
| url = http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html
}}
</ref> and Snyder<ref name = "SnyderBarzilay07">
{{cite conference
| first1 = Benjamin | last1 = Snyder
| first2 = Regina | last2 = Barzilay
| title = Multiple Aspect Ranking using the Good Grief Algorithm
| book-title = Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL)
| year = 2007
| pages = 300–307
| url = http://people.csail.mit.edu/regina/my_papers/ggranker.ps
}}
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref name=":6">Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>

在2004年AAAI春季研讨会上，语言学家、计算机科学家和其他感兴趣的研究人员首次将各种方法——学习、词汇、基于知识等——结合起来，提出了共享任务和基准数据集，以便对文本中的情感、吸引力、主观性和情感进行系统的计算研究。<ref name=":6" />

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.Moreover, it can be proven that specific classifiers such as the [[Maximum entropy probability distribution|Max Entropy]]<ref name = "Vryniotis13">
{{cite conference
| first = Vasilis | last = Vryniotis
| title = The importance of Neutral Class in Sentiment Analysis
| year = 2013
| url = http://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
}}
</ref> and [[Support vector machine|SVMs]]<ref name = "KoppelSchler06">
{{cite conference
| first1 = Moshe | last1 = Koppel
| first2 = Jonathan | last2 = Schler
| title = The Importance of Neutral Examples for Learning Sentiment
| book-title = Computational Intelligence 22
| year = 2006
| pages = 100–109
| citeseerx = 10.1.1.84.9735
}}
</ref> can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step.This second approach often involves estimating a probability distribution over all categories (e.g. [[Naive Bayes classifier|naive Bayes]] classifiers as implemented by the [[Nltk|NLTK]]).Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

尽管在大多数统计分类方法中，根据中性文本位于二元分类器边界附近的假设，中性类常常忽略了，但一些研究者建议在每个极性问题中必须确定三个类别。此外，研究也证明引入中立类可以提高某些分类器的整体准确率，如最大熵（Max Entropy）<ref name="Vryniotis13" /> 和支持向量机（SVMs）<ref name="KoppelSchler06" /> 等特定分类器。原则上由两种方法可以进行中性分类。一是，算法首先识别出中性分类后将其过滤，再根据正极性和负极性的情感二分类对其他内容进行评估。二是，一步构建包含中性、正极性和负极性三种类别的分类。<ref>{{Cite journal|last1=Ribeiro|first1=Filipe Nunes|last2=Araujo|first2=Matheus|date=2010|title=A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods|url=https://www.researchgate.net/publication/286302059|journal=Transactions on Embedded Computing Systems |volume=9 |issue=4}}</ref> 第二种方法通常会涉及到估计所有类别的概率分布（比如[[Nltk|NLTK]]实现的naive Bayes分类器）。是否以及如何使用中性分类取决于数据的性质：如果数据被清晰地分类为中性、正极性和负极性的语言，那么过滤掉中性语言并关注正极性和负极性情感的极性是有意义的。相比之下，如果数据大部分是中性的，对正极性和负极性影响的偏差很小，这种策略就会使其更难明确区分两极。

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment with them are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using [[natural language processing]], each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score.<ref>{{Cite journal|last1=Taboada|first1=Maite|last2=Brooke|first2=Julian|date=2011|title=Lexicon-based methods for sentiment analysis|url=http://dl.acm.org/citation.cfm?id=2000518|journal=Computational Linguistics |volume=37 |issue=2 |pages=272–274|doi=10.1162/coli_a_00049|citeseerx=10.1.1.188.5517|s2cid=3181362}}</ref><ref>{{Cite journal|last1=Augustyniak|first1=Łukasz|last2=Szymański|first2=Piotr|last3=Kajdanowicz|first3=Tomasz|last4=Tuligłowicz|first4=Włodzimierz|date=2015-12-25|title=Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis|journal=Entropy|language=en|volume=18|issue=1|pages=4|doi=10.3390/e18010004|bibcode=2015Entrp..18....4A|doi-access=free}}</ref><ref>{{Cite journal|last1=Mehmood|first1=Yasir|last2=Balakrishnan|first2=Vimala|date=2020-01-01|title=An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration|url=https://doi.org/10.1108/OIR-10-2018-0295|journal=Online Information Review|volume=44|issue=5|pages=1097–1117|doi=10.1108/OIR-10-2018-0295|issn=1468-4527}}</ref> This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.<ref name ="SentiStrength2010">
{{cite journal
| first1 = Mike
| last1 = Thelwall
| first2 = Kevan
| last2 = Buckley
| first3 = Georgios
| last3 = Paltoglou
| first4 = Di
| last4 = Cai
| first5 = Arvid
| last5 = Kappas
| title = Sentiment strength detection in short informal text
| year = 2010
| journal = Journal of the American Society for Information Science and Technology
| volume= 61
| issue= 12
| pages= 2544–2558
| url = http://www.scit.wlv.ac.uk/~cm1993/papers/SentiStrengthPreprint.doc
| doi=10.1002/asi.21416
| citeseerx = 10.1.1.278.3863
}}
</ref>

另一种确定情绪的方法是使用一种比例系统，根据这种比例系统，通常与负面、中性或正面情绪相关的词在 -10到 + 10的范围内(大多数从负面到最正面)或从0到正面上限(如 + 4)被赋予一个相关的数字。这样就可以根据环境调整特定术语的情绪(通常是在句子的层面上)。当使用自然语言处理对一篇非结构化文本进行分析时，基于情感词与概念及其相关得分的关系，给特定环境中的每个概念打分。这使得人们可以对情绪有更深入的理解，因为现在可以调整一个概念的情绪价值，相对于它周围可能发生的变化。例如，强化、放松或否定概念所表达的情感的词语会影响它的得分。或者，如果文本的目标是确定文本中的情绪，而不是文本的总体极性和强度，那么文本可以给出积极和消极的情绪强度评分。

There are various other types of sentiment analysis like- Aspect Based sentiment analysis, Grading sentiment analysis (positive,negative,neutral), Multilingual sentiment analysis and detection of emotions.

还有各种其他类型的情感分析，如基于方面的情感分析，分级情感分析(积极的，消极的，中性的) ，多语言情感分析和情感检测。

=== Subjectivity/objectivity identification ===
This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.<ref name="PangLee08Subjectivity">{{cite book
| first1 = Bo
| last1 = Pang
| first2 = Lillian
| last2 = Lee
| title = Opinion Mining and Sentiment Analysis
| year = 2008
| publisher = Now Publishers Inc
| chapter = 4.1.2 Subjectivity Detection and Opinion Identification
| chapter-url = http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html
}}
</ref> This problem can sometimes be more difficult than polarity classification.<ref name="MihalceaAl07">{{cite conference
|first1 = Rada
|last1 = Mihalcea
|first2 = Carmen
|last2 = Banea
|first3 = Janyce
|last3 = Wiebe
|title = Learning Multilingual Subjective Language via Cross-Lingual Projections
|book-title = Proceedings of the Association for Computational Linguistics (ACL)
|year = 2007
|pages = 976–983
|url = http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20100708065222/http://www.cse.unt.edu/~rada/papers/mihalcea.acl07.pdf
|archive-date = 2010-07-08
}}
</ref> The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,<ref name="SuMarkert08">{{cite conference
| first1 = Fangzhong | last1 = Su
| first2 = Katja | last2 = Markert
| title = From Words to Senses: a Case Study in Subjectivity Recognition
| book-title = Proceedings of Coling 2008, Manchester, UK
| year = 2008
| url = http://www.comp.leeds.ac.uk/markert/Papers/Coling2008.pdf
}}
</ref> results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang<ref name="PangLee04">{{cite conference
| first1 = Bo | last1 = Pang
| first2 = Lillian | last2 = Lee
| title = A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
| book-title = Proceedings of the Association for Computational Linguistics (ACL)
| year = 2004
| pages = 271–278
| url = http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html
}}
</ref> showed that removing objective sentences from a document before classifying its polarity helped improve performance.

这个任务通常被定义为将一个给定的文本(通常是一个句子)分成两类: 客观的或主观的。这个问题有时比极性分类更难解决。词汇和短语的主观性可能取决于它们的上下文，客观文件可能包含主观句子(例如，一篇引用人们观点的新闻文章)。此外，正如苏所提到的，结果在很大程度上依赖于注释文本时使用的主观性的定义。然而，彭日成表示，在对文件进行分类之前，去掉文件中的客观句子有助于提高表现。

The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>

客观这个术语指的是携带事实信息的事件。

* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

* 客观句的例子:”要当选美国总统，候选人必须年满35岁。'

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions.Also known as 'private states' mentioned by Quirk et al.<ref>{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'.  Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />

主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被称为私有状态。在下面的例子中，它反映了一个私人国家“我们美国人”。此外，被评论的目标实体可以采取从有形产品到刘(2010)所述无形话题事项的多种形式。此外，刘(2010)观察到三种态度: 1)积极的观点，2)中立的观点，3)消极的观点。

* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子: 我们美国人需要选出一位成熟且能够做出明智决定的总统。'这种分析是一个分类问题。
Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref>{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

每个类的单词或短语指示符的集合被定义用于在未注释的文本上定位理想的模式。对于主观表达，已经创建了一个不同的词表。单词或短语中的主观指标列表是由 Riloff 语言学家和自然语言处理领域的多名研究人员开发的。必须创建一个抽取规则字典来度量给定的表达式。多年来，在主观检测方面，从1999年的手工特征提取到2005年的自动特征学习。目前，自动化学习方法可以进一步分为监督学习和非监督式学习学习。利用机器学习过程对文本进行注释和去注释的模式提取方法已经成为学术界研究的热点。

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到，在为表达式制定一套固定的规则方面存在一些挑战。规则开发中的许多挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战: 1)隐喻性的表达方式，2)写作中的差异，3)上下文敏感性，4)代表用法较少的单词，5)时间敏感性，以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref>{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
# Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles
# Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.<ref name=":1">{{Cite journal|last1=Pang|first1=Bo|last2=Lee|first2=Lillian|date=2008-07-06|title=Opinion Mining and Sentiment Analysis|url=https://www.nowpublishers.com/article/Details/INR-011|journal=Foundations and Trends in Information Retrieval|language=en|volume=2|issue=1–2|pages=1–135|doi=10.1561/1500000011|issn=1554-0669}}</ref>
# Time-sensitive attribute. The task is challenged by the some textual data’s time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
# Cue words with fewer usages.
# Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

# 比喻性的表达。文本中包含的隐喻表达可能会影响抽取的性能。此外，隐喻采取不同的形式，这可能有助于增加检测。# 文字上的差异。对于从互联网上获得的文本，目标文本数据的写作风格差异涉及不同的写作类型和风格 # 上下文敏感。分类可以根据前面和后面句子的主观性或客观性而有所不同。# 时间敏感属性。该任务受到某些文本数据的时间敏感属性的挑战。如果一群研究人员想要在新闻中证实一个事实，他们需要更长的时间，比新闻变得过时更长的交叉验证。# 暗示用词较少的词语。# 不断增长的数量。这项任务还受到大量文本数据的挑战。文本数据的不断增长性使得研究人员很难按时完成任务。

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
# Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
# Time-consuming. Manual annotation task is an assiduious work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.<ref>{{Cite journal|last=Riloff|first=Ellen|date=1996-08-01|title=An empirical study of automated dictionary construction for information extraction in three domains|url=https://dx.doi.org/10.1016%2F0004-3702%2895%2900123-9|journal=Artificial Intelligence|language=en|volume=85|issue=1|pages=101–134|doi=10.1016/0004-3702(95)00123-9|issn=0004-3702|doi-access=free}}</ref>

# 理解上的变化。在手工注释过程中，由于语言的模糊性，注释者之间可能会出现主观或客观实例的分歧。# 人为错误。手工注释是一项细致的工作，需要高度集中精力才能完成。# 费时。手工注释是一项繁重的工作。里洛夫(1996)表明，一个注释者完成160篇文本需要8个小时.
All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

所有这些原因都会影响主客观分类的效率和有效性。相应地，设计了两种自举方法来从未注释的文本数据中学习语言模式。两种方法都以少量种子词和未注释的文本数据开始。

# Meta-Bootstrapping by Riloff and Jones in 1999.<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Jones|first2=Rosie|date=July 1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|url=https://aaai.org/Papers/AAAI/1999/AAAI99-068.pdf|journal=AAAI '99/IAAI '99: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence|pages=474–479}}</ref> Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Leve Two: Top 5 words will be marked and add to the dictionary. Repeat.
# Basilisk (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge) by Thelen and Riloff.<ref>{{Cite journal|last1=Thelen|first1=Michael|last2=Riloff|first2=Ellen|date=2002-07-06|title=A bootstrapping method for learning semantic lexicons using extraction pattern contexts|journal=Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10|series=EMNLP '02|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=214–221|doi=10.3115/1118693.1118721|s2cid=137155|doi-access=free}}</ref> Step One: Generate extration patterns Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

# 1999年里洛夫和琼斯的 Meta-Bootstrapping。第一级: 根据预定义的规则生成提取模式，并根据每个模式所包含的种子词数量生成提取模式。第二步: 前5个单词将被标记并添加到字典中。重复。# Basilisk (Bootstrapping Approach to SemantIc Lexicon inducing using SemantIc Knowledge) Thelen and Riloff.第一步: 生成抽取模式第二步: 将最好的模式从模式池移动到候选单词池。第三步: 将前10个单词标记并添加到字典中。重复。

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

总之，这些算法突出了主客观任务中模式自动识别和提取的需要。

Subjective and object classifier can enhance the serval applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries.  According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.<ref>{{Cite journal|last=Liu|first=Bing|date=2012-05-23|title=Sentiment Analysis and Opinion Mining|url=https://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016|journal=Synthesis Lectures on Human Language Technologies|volume=5|issue=1|pages=1–167|doi=10.2200/S00416ED1V01Y201204HLT016|issn=1947-4040}}</ref>

主观分类器和对象分类器可以增强自然语言处理的一些应用。分类器的主要好处之一是它使数据驱动决策过程的实践在各个行业中普及。据刘说，主观和客观识别的应用已经在商业、广告、体育和社会科学中得到了实施。

* Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
* Stock price prediction: In the finance industry, the classier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et.al. indicates that model with subjective and objective module may perform better than those without this part.<ref>{{Cite journal|last1=Deng|first1=Shangkun|last2=Mitsubuchi|first2=Takashi|last3=Shioda|first3=Kei|last4=Shimada|first4=Tatsuro|last5=Sakurai|first5=Akito|date=December 2011|title=Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction|url=http://dx.doi.org/10.1109/dasc.2011.138|journal=2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing|pages=800–807|publisher=IEEE|doi=10.1109/dasc.2011.138|isbn=978-1-4673-0006-3|s2cid=15262023}}</ref>
* Social media analysis.
* Students' feedback classification.<ref>{{Cite journal|last1=Nguyen|first1=Kiet Van|last2=Nguyen|first2=Vu Duc|last3=Nguyen|first3=Phu X.V.|last4=Truong|first4=Tham T.H.|last5=Nguyen|first5=Ngan L-T.|date=2018-10-01|title=UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis|url=https://ieeexplore.ieee.org/document/8573337|journal=2018 10th International Conference on Knowledge and Systems Engineering (KSE)|pages=19–24|location=Vietnam|publisher=IEEE|doi=10.1109/KSE.2018.8573337|isbn=978-1-5386-6113-0}}</ref>
*Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
* Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.<ref>{{Cite journal|last1=Yu|first1=Hong|last2=Hatzivassiloglou|first2=Vasileios|date=2003-07-11|title=Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|location=USA|publisher=Association for Computational Linguistics|pages=129–136|doi=10.3115/1119355.1119372|doi-access=free}}</ref>
* Domain-specific applications.
* Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

* 在线评论分类: 在商业行业，分类器帮助公司更好地了解产品的反馈和评论背后的原因。
* 股票价格预测: 在金融行业，分类器通过从社会媒体获得的过程辅助信息和从互联网获得的其他文本信息来辅助预测模型。以往对日本股票价格的研究都是由 Dong et.al 进行的。表明具有主客观模块的模型可能比没有主客观模块的模型表现更好。
* 社交媒体分析。
* 学生意见分类。
* 文件总结: 分类器可以提取目标特定的评论，并收集一个特定实体的意见。
* 复杂问题回答。量词可以通过对语言主题或客观目标进行分类来解析复杂问题。在余等人的研究中。(2003) ，研究人员开发了一个句子和文档级别的群集身份意见片。
* 特定领域的应用程序。
* 电子邮件分析: 主观和客观分类器通过追踪目标单词的语言模式来检测垃圾邮件。

=== Feature/aspect-based ===
It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.<ref name="HuLiu04">{{cite conference
| first1 = Minqing | last1 = Hu
| first2 = Bing | last2 = Liu
| title = Mining and Summarizing Customer Reviews
| book-title = Proceedings of KDD 2004.
| year = 2004
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.<ref>{{Cite journal|title = Good location, terrible food: detecting feature sentiment in user-generated reviews|journal = Social Network Analysis and Mining|date = 2013-06-22|issn = 1869-5450|pages = 1149–1163|volume = 3|issue = 4|doi = 10.1007/s13278-013-0119-7|first1 = Mario|last1 = Cataldi|first2 = Andrea|last2 = Ballatore|first3 = Ilaria|last3 = Tiddi|first4 = Marie-Aude|last4 = Aufaure|citeseerx = 10.1.1.396.9313|s2cid = 5025282}}</ref> This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.<ref name="LiuHuCheng04">{{cite conference
| first1 = Bing | last1 = Liu
| first2 = Minqing | last2 = Hu | first3 = Junsheng | last3 = Cheng
| title = Opinion Observer: Analyzing and Comparing Opinions on the Web
| book-title = Proceedings of WWW 2005.
| year = 2005
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
</ref> The automatic identification of features can be performed with syntactic methods, with [[topic model]]ing,<ref>{{Cite book|title = Constrained LDA for Grouping Product Features in Opinion Mining|publisher = Springer Berlin Heidelberg|date = 2011-01-01|isbn = 978-3-642-20840-9|pages = 448–459|series = Lecture Notes in Computer Science|doi = 10.1007/978-3-642-20841-6_37|first1 = Zhongwu|last1 = Zhai|first2 = Bing|last2 = Liu|first3 = Hua|last3 = Xu|first4 = Peifa|last4 = Jia|editor-first = Joshua Zhexue|editor-last = Huang|editor-first2 = Longbing|editor-last2 = Cao|editor-first3 = Jaideep|editor-last3 = Srivastava|citeseerx = 10.1.1.221.5178}}</ref><ref>{{Cite book|title = Modeling Online Reviews with Multi-grain Topic Models|publisher = ACM|journal = Proceedings of the 17th International Conference on World Wide Web|date = 2008-01-01|location = New York, NY, USA|isbn = 978-1-60558-085-2|pages = 111–120|series = WWW '08|doi = 10.1145/1367497.1367513|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063|s2cid = 13609860}}</ref> or with [[deep learning]].<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| year = 2016
| journal = Knowledge-Based Systems
| volume= 108
| pages= 42–49
| doi=10.1016/j.knosys.2016.06.009
}}
</ref><ref name="Ma">{{cite conference
| first = Yukun | last = Ma | display-authors=etal
| title = Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM
| year = 2018
| book-title = Proceedings of AAAI
| pages= 5876–5883
}}
</ref> More detailed discussions about this level of sentiment analysis can be found in Liu's work.<ref name="Liu2010">{{cite conference
| first = Bing | last = Liu
| title = Sentiment Analysis and Subjectivity
| book-title = Handbook of Natural Language Processing | edition = Second | editor1-first = N. | editor1-last = Indurkhya | editor2-first = F. J. | editor2-last = Damerau
| date = 2010
| url = http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
}}
</ref>

它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。

== Methods and features方法和特征 ==

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
| first1 = E
| last1 = Cambria
| first2 = B
| last2 = Schuller
| first3 = Y
| last3 = Xia
| first4 = C
| last4 = Havasi
| title = New avenues in opinion mining and sentiment analysis
| year = 2013
| journal = IEEE Intelligent Systems
| volume= 28
| issue= 2
| pages= 15–21
| doi=10.1109/MIS.2013.30
| citeseerx = 10.1.1.688.1384
| s2cid = 12104996
}}
</ref> Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.<ref name="Ortony">
{{cite book
|first1 = Andrew
|last1 = Ortony
|first2 = G
|last2 = Clore
|first3 = A
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|year = 1988
|url = http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|publisher = Cambridge Univ. Press
|url-status = dead
|archive-url = https://web.archive.org/web/20151123055038/http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf
|archive-date = 2015-11-23
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
| first1 = Ryan
| last1 = Stevenson
| first2 = Joseph
| last2 = Mikels
| first3 = Thomas
| last3 = James
| title = Characterization of the Affective Norms for English Words by Discrete Emotional Categories
| year = 2007
| journal = Behavior Research Methods
| volume= 39
| issue= 4
| pages= 1020–1024
| url = http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf
| pmid = 18183921
| doi=10.3758/bf03192999
| s2cid = 6673690
| doi-access = free
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
| first = Peter | last = Turney
| title = Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
| book-title = Proceedings of the Association for Computational Linguistics
| pages = 417–424
| year = 2002
| arxiv = cs.LG/0212032
}}
</ref> and [[deep learning]]. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt).<ref name="Kim+Hovy06">
{{cite conference
|last1 = Kim
|first1 = S. M.
|last2 = Hovy
|first2 = E. H.
|title = Identifying and Analyzing Judgment Opinions.
|book-title = Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY.
|year = 2006
|url = http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|archive-url = https://web.archive.org/web/20110629121135/http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
|url-status = dead
|archive-date = 2011-06-29
}}
</ref> To mine the opinion in [[Context (language use)|context]] and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.<ref name="DeyHaque08">
{{cite conference
| first1 = Lipika | last1 = Dey | first2 = S. K. Mirajul | last2 = Haque
| title = Opinion Mining from Noisy Text Data
| book-title = Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90
| year = 2008
| url = http://portal.acm.org/citation.cfm?id=1390763&dl=GUIDE&coll=GUIDE&CFID=92244761&CFTOKEN=30578437
}}
</ref> Hybrid approaches leverage both machine learning and elements from [[knowledge representation]] such as [[ontologies]] and [[semantic network]]s in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.<ref name="“Hussain">
{{cite book
| first1 = E
| last1 = Cambria
| first2 = A
| last2 = Hussain
| title = Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis
| year = 2015
| url = http://springer.com/9783319236544
| publisher = Springer
| isbn = 9783319236544
}}
</ref>

现有的情感分析方法可以分为三大类: 基于知识的技术、统计方法和混合方法。基于知识的技术根据明确的情感词汇的出现，如高兴、悲伤、害怕和无聊，按照情感类别对文本进行分类。一些知识库不仅列出了明显的情感词汇，而且还赋予任意的词汇一种可能的特定情感的“亲和力”。统计方法利用机器学习中的元素，例如潜在语义学、支持向量机、“单词包”、语义定位的“点间互信息”和深度学习。更复杂的方法试图检测情绪的持有者(即保持情绪状态的人)和目标(即感受情绪的实体)。为了在上下文中挖掘观点，得到说话人的观点，使用了词语的语法关系。语法依存关系是通过对文本的深入分析得到的。混合方法利用机器学习和来自知识表示的元素，如本体论和语义网络，以便检测以微妙的方式表示的语义，例如，通过分析没有明确传达相关信息，但是隐含链接到这样做的其他概念的概念。

Open source software tools as well as range of free and paid sentiment analysis tools deploy [[machine learning]], statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.<ref name="AkcoraBayirDemirbasFerhatosmanoglu2010">
{{cite conference
| first1 = Cuneyt Gurcan | last1 = Akcora | first2 = Murat Ali | last2 = Bayir | first3 = Murat | last3 = Demirbas | first4 = Hakan | last4 = Ferhatosmanoglu
| title = Identifying breakpoints in public opinion
| book-title = SigKDD, Proceedings of the First Workshop on Social Media Analytics
| year = 2010
| url = http://portal.acm.org/citation.cfm?id=1964867
}}
</ref> Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective [[commonsense reasoning]].<ref>{{Cite journal|last1=Sasikala|first1=P.|last2=Mary Immaculate Sheela|first2=L.|date=December 2020|title=Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS|journal=Journal of Big Data|language=en|volume=7|issue=1|pages=33|doi=10.1186/s40537-020-00308-7|issn=2196-1115|doi-access=free}}</ref> Sentiment analysis can also be performed on visual content, i.e., images and videos (see [[Multimodal sentiment analysis]]). One of the first approaches in this direction is SentiBank<ref name = "Borth13">
{{cite conference
| first1 = Damian | last1 = Borth
| first2 = Rongrong | last2 = Ji | first3 = Tao | last3 = Chen | first4 = Thomas | last4 = Breuel | first5 = Shih-Fu | last5 = Chang
| title = Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs
| book-title = Proceedings of ACM Int. Conference on Multimedia
| pages = 223–232
| year = 2013
| url = https://visual-sentiment-ontology.appspot.com
}}
</ref> utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, [[grammar]] and even [[word order]]. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result,<ref>{{Cite journal|last1=Socher|first1=Richard|last2=Perelygin|first2=Alex|last3=Wu|first3=Jean Y.|last4=Chuang|first4=Jason|last5=Manning|first5=Christopher D.|last6=Ng|first6=Andrew Y.|last7=Potts|first7=Christopher|date=2013|title=Recursive deep models for semantic compositionality over a sentiment treebank|journal=In Proceedings of EMNLP|pages=1631–1642|citeseerx=10.1.1.593.7427}}</ref> but they incur an additional annotation overhead.

开源软件工具以及一系列免费和付费的情绪分析工具利用机器学习、统计学和自然语言处理技术，对大量文本自动进行情绪分析，这些文本包括网页、在线新闻、互联网讨论组、在线评论、网络博客和社交媒体。另一方面，知识推理系统则利用公开的资源，提取与自然语言概念相关的语义和情感信息。该系统可以帮助执行情感常识推理。情感分析也可以在可视内容上执行，例如，图像和视频(请参阅 Multimodal 情感分析)。这方面的第一个方法是使用形容词名词对表示视觉内容。此外，绝大多数情感分类方法都依赖于情感分类词袋模型，它忽略了上下文、语法甚至词序。基于词语组成长短语意义的情感分析方法取得了较好的效果，但也增加了额外的注释开销。

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans.<ref>{{cite web|title=Case Study: Advanced Sentiment Analysis|url=http://paragonpoll.com/sentiment-analysis-systems-case-study/|access-date=18 October 2013}}</ref> However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.<ref>{{Cite journal|last1=Mozetič|first1=Igor|last2=Grčar|first2=Miha|last3=Smailović|first3=Jasmina|date=2016-05-05|title=Multilingual Twitter Sentiment Classification: The Role of Human Annotators|journal=PLOS ONE|volume=11|issue=5|pages=e0155036|doi=10.1371/journal.pone.0155036|issn=1932-6203|pmc=4858191|pmid=27149621|arxiv=1602.07563|bibcode=2016PLoSO..1155036M}}</ref>

在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。

== Evaluation 评估 ==

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
{{cite news
| last = Ogneva | first = M.
| title = How Companies Can Use Sentiment Analysis to Improve Their Business
| url=http://mashable.com/2010/04/19/sentiment-analysis/ | publisher = Mashable
|access-date=2012-12-13}}
</ref> of the time (see [[Inter-rater reliability]]). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about ''any'' answer.<ref>
{{cite book
| last = Roebuck | first = K.
| title = Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors
| url=https://books.google.com/books?id=kqsNBwAAQBAJ| isbn = 9781743049457
| date = 2012-10-24
}}
</ref>

情感分析系统的准确性，原则上来说，就是它与人类判断的一致程度。这通常是衡量的不同措施的基础上的准确率召回率，超过两个目标类别的消极和积极的文本。然而，根据研究，人类评分员通常只有80% 的时间同意(参见评分员之间的可靠性)。因此，一个在分类情绪上达到70% 准确率的程序几乎和人类一样好，即使这样的准确率听起来并不令人印象深刻。如果一个程序在100% 的时间里是“正确的”，人类仍然会在20% 的时间里不同意它，因为他们对任何答案都有很大的不同意见。

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, [[joke]]s, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.<ref>
[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "The relation between author mood and affect to sentiment in text and text genre." In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval, pp. 9-10. ACM, 2011.
</ref><ref>
[[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/ImageCLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.
</ref>

另一方面，计算机系统会犯与人类评估员非常不同的错误，因此这些数字并不完全可比。例如，计算机系统在否定、夸张、笑话或讽刺方面会遇到麻烦，而这些对于人类读者来说通常是很容易处理的: 计算机系统出现的一些错误对于人类来说会显得过于天真。一般来说，学术研究中定义的情绪分析对实际商业任务的效用受到质疑，主要是因为简单的从消极到积极的情绪单维度模型产生的可操作信息很少，客户担心公共话语对情绪分析的影响。品牌或企业声誉。Karlgren, Jussi, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors.“情绪分析的有用性。”在欧洲信息检索会议上，pp。426-435.Springer Berlin Heidelberg，2012年。尤西 · 卡尔格伦。作者情绪与文本和文本体裁中情感的关系在《第四次研讨会论文集---- 开发信息检索语义标注》中，pp。9-10.美国计算机协会，2011。尤西 · 卡尔格伦。影响与多媒体信息互动的因素包括情感、吸引力和情感在 Theseus/ImageCLEF 视觉信息检索评估研讨会论文集中，第页。8-11.2009.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on [[brand image|brand reputation]].<ref>
Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and [[Maarten de Rijke]]. "Overview of RepLab 2012: Evaluating Online Reputation Management Systems." In CLEF (Online Working Notes/Labs/Workshop). 2012.
</ref><ref>
Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2013: Evaluating online reputation monitoring systems." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 333-352. Springer Berlin Heidelberg, 2013.
</ref><ref name="replab2014">
Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, [[Maarten de Rijke]], and Damiano Spina. "Overview of replab 2014: author profiling and reputation dimensions for online reputation management." In International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 307-322. Springer International Publishing, 2014.
</ref>

为了更好地适应市场需求，情绪分析的评估已转向更多基于任务的措施，与公关机构和市场研究专业人士的代表共同制定。中的焦点。RepLab 评估数据集较少考虑文本的内容，而更多考虑文本对品牌声誉的影响。Amigó, Enrique, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke.“ RepLab 2012概述: 评估在线信誉管理系统”在 CLEF (网上工作笔记/实验室/工作坊)。2012.Amigó, Enrique, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2013概述: 评估在线声誉监控系统。”欧洲语言跨语言评价论坛国际会议，第页。333-352.Springer Berlin Heidelberg，2013年。Amigó, Enrique, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, and Damiano Spina.“ replab 2014概述: 在线声誉管理的作者特征和声誉维度。”欧洲语言跨语言评价论坛国际会议，第页。307-322.斯普林格国际出版社，2014年。

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

由于情感分析的评价越来越多地基于任务，每个实现都需要一个单独的训练模型来更准确地表达给定数据集的情感。

== Web 2.0 ==
{{See also|Reputation management|web 2.0|web mining}}
The rise of [[social media]] such as [[blogs]] and [[social network]]s has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis.<ref name="Mining the Web for Feelings, Not Facts">Wright, Alex. [https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1 "Mining the Web for Feelings, Not Facts"], ''[[New York Times]]'', 2009-08-23. Retrieved on 2009-10-01.</ref> Further complicating the matter, is the rise of anonymous social media platforms such as [[4chan]] and [[Reddit]].<ref>{{cite web|title=Sentiment Analysis on Reddit|url=http://news.humanele.com/sentiment-analysis-reddit/|access-date=10 October 2014|date=2014-09-30}}</ref> If [[web 2.0]] was all about democratizing publishing, then the next stage of the web may well be based on democratizing [[data mining]] of all the content that is getting published.<ref name="The Future of Social Media Monitoring">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''[[ReadWriteWeb]]'', 2009-04-15. Retrieved on 2009-10-01.</ref>

博客和社交网络等社交媒体的兴起激发了人们对情绪分析的兴趣。随着评论、评级、推荐和其他形式的网络表达的激增，网络舆论已经变成了一种虚拟货币，企业可以通过这种货币来推销自己的产品、寻找新的机会和管理自己的声誉。随着企业寻求自动化过滤噪音的过程，理解对话，识别相关内容并适当活动，许多企业现在正在寻找情绪分析领域。莱特，亚历克斯。“从网上挖掘情感，而不是事实”，纽约时报，2009-08-23。2009-10-01.更复杂的是，匿名社交媒体平台的兴起，如4chan 和 Reddit。如果说 web 2.0完全是关于民主化发布，那么 web 的下一个阶段很可能是基于对所有正在发布的内容的民主化数据挖掘。马歇尔 · 柯克帕特里克。”，ReadWriteWeb，2009-04-15。2009-10-01.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in [[Virtual community|e-communities]] through sentiment analysis.<ref name="Collective emotions in cyberspace">CORDIS. [http://cordis.europa.eu/fetch?CALLER=FP7_PROJ_EN&ACTION=D&DOC=1&CAT=PROJ&QUERY=011e4ea33ef2:358b:41dc0328&RCN=89032 "Collective emotions in cyberspace (CYBEREMOTIONS)"], ''[[European Commission]]'', 2009-02-03. Retrieved on 2010-12-13.</ref> The [[CyberEmotions|CyberEmotions project]], for instance, recently identified the role of negative [[emotion]]s in driving social networks discussions.<ref name="NewSci_flaming">Condliffe, Jamie. [https://www.newscientist.com/article/dn19821-flaming-drives-online-social-networks.html "Flaming drives online social networks "], ''[[New Scientist]]'', 2010-12-07. Retrieved on 2010-12-13.</ref>

实现这一目标的一个步骤就是研究。目前，世界各地的一些大学的研究团队通过情绪分析专注于了解电子社区中情绪的动态。「网络空间的集体情绪」，欧洲委员会，2009-02-03。2010-12-13.例如，CyberEmotions 项目最近发现了负面情绪在推动社交网络讨论中的作用。“火焰驱动在线社交网络”，《新科学家》，2010-12-07。2010-12-13.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment.<ref name="Mining the Web for Feelings, Not Facts" /> The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

问题是，大多数情绪分析算法使用简单的术语来表达对产品或服务的情绪。然而，文化因素、语言上的细微差别以及不同的语境使得将一串文字转换成简单的赞成或反对的情绪变得极其困难。事实上，人们经常不同意文本的情绪，这说明了计算机要做好这件事是多么艰巨的任务。字符串越短，就越难。

Even though short text strings might be a problem, sentiment analysis within [[microblogging]] has shown that [[Twitter]] can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.<ref>Tumasjan, Andranik; O.Sprenger, Timm; G.Sandner, Philipp; M.Welpe, Isabell (2010). [http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"]. "Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media"</ref> Furthermore, sentiment analysis on [[Twitter]] has also been shown to capture the public mood behind human reproduction cycles on a planetary scale{{peacock term|date=June 2018}},<ref name="r25">{{cite journal|doi=10.1038/s41598-017-18262-5|pmid=29269945|pmc=5740080|title=Human Sexual Cycles are Driven by Culture and Match Collective Moods|journal=Scientific Reports|volume=7|issue=1|pages=17973|year=2017|last1=Wood|first1=Ian B.|last2=Varela|first2=Pedro L.|last3=Bollen|first3=Johan|last4=Rocha|first4=Luis M.|last5=Gonçalves-Sá|first5=Joana|bibcode=2017NatSR...717973W|arxiv=1707.03959}}</ref> as well as other problems of public-health relevance such as adverse drug reactions.<ref name="r27">{{cite journal|doi=10.1016/j.jbi.2016.06.007|pmid=27363901|pmc=4981644|title=Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts|journal=Journal of Biomedical Informatics|volume=62|pages=148–158|year=2016|last1=Korkontzelos|first1=Ioannis|last2=Nikfarjam|first2=Azadeh|last3=Shardlow|first3=Matthew|last4=Sarker|first4=Abeed|last5=Ananiadou|first5=Sophia|last6=Gonzalez|first6=Graciela H.}}</ref>

尽管短文字符串可能是个问题，微博内的情绪分析已经表明，Twitter 可以被视为一个有效的政治情绪在线指标。推特的政治情绪表明，它与政党和政客的政治立场非常吻合，这表明推特信息的内容合理地反映了线下的政治格局。安德拉尼克; O.Sprenger，Timm; G.Sandner，Philipp; M.Welpe，Isabell (2010)。“用 Twitter 预测选举: 140个人物揭示的政治情绪”。此外，推特上的情绪分析还显示，在全球范围内，人类生殖周期背后的公众情绪，以及其他与公共健康相关的问题，如药物不良反应。

== Application in recommender systems ==
{{See also|Recommender system}}
For a [[recommender system]], sentiment analysis has been proven to be a valuable technique. A [[recommender system]] aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, [[collaborative filtering]] works on the rating matrix, and [[content-based filtering]] works on the [[Metadata|meta-data]] of the items.

一个推荐系统以来，情绪分析已经被证明是一种有价值的技术。推荐系统的目的是预测目标用户对某个商品的偏好。主流推荐系统工作在显式数据集上。例如，协同过滤工作在评级矩阵上，基于内容的过滤工作在项目的元数据上。

In many [[social networking service]]s or [[e-commerce]] websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature.<ref>{{cite journal|url=https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|archive-url=https://web.archive.org/web/20180524004208/https://pdfs.semanticscholar.org/8f1b/9b97183b8aa2caa0fb6c9563b14daabe8316.pdf|url-status=dead|archive-date=2018-05-24|first1=Huifeng|last1=Tang|first2=Songbo|last2=Tan|first3=Xueqi|last3=Cheng|title=A survey on sentiment detection of reviews|journal=Expert Systems with Applications|volume=36|issue=7|year=2009|pages=10760–10773|doi=10.1016/j.eswa.2009.02.063|s2cid=2178380}}</ref> The item's feature/aspects described in the text play the same role with the meta-data in [[content-based filtering]], but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

在许多社交网络服务或电子商务网站，用户可以提供文本审查，评论或反馈的项目。这些用户生成的文本提供了丰富的来源，用户对许多产品和项目的情感意见。对于一个项目，这样的文本可以显示项目的相关特性/方面以及用户对每个特性的看法。在基于内容的过滤中，文本中描述的条目的特征/方面与元数据起着同样的作用，但前者对推荐系统更有价值。由于用户在评论中广泛提到这些功能，它们可以被视为能够显著影响用户对产品的体验的最关键的功能，而产品的元数据(通常由生产者而不是消费者提供)可能忽略用户关心的功能。对于具有共同特征的不同项目，用户可能会给出不同的感受。而且，同一个项目的某个特性可能会收到不同用户的不同意见。用户对特征的感受可以看作是一个多维度的评分分值，反映了用户对特征的偏好。

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed.<ref name=":0">Jakob, Niklas, et al. "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations." ''Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion''. ACM, 2009.</ref> There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items,<ref>{{cite journal|first1=Hu|last1=Minqing|first2=Bing|last2=Liu|title=Mining opinion features in customer reviews|journal=AAAI|volume=4|issue=4|year=2004|s2cid=5724860|url=https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|archive-url=https://web.archive.org/web/20180524004041/https://pdfs.semanticscholar.org/ee6c/726b55c66d4c222556cfae62a4eb69aa86b7.pdf|url-status=dead|archive-date=2018-05-24}}</ref> while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.<ref name=":0" />

基于特征/方面和从用户生成的文本中提取的情感，可以构造一个混合推荐系统。雅各布，尼克拉斯，等等。“超越明星: 利用免费文本用户评论来提高电影推荐的准确性。”第一届国际信息和通信技术会议论文集——民意情绪分析。美国计算机协会，2009。向用户推荐候选商品有两种动机。第一个动机是候选项目与用户偏好项目具有许多共同特征，第二个动机是候选项目对其特征的高度评价。对于一个首选项目，有理由相信具有相同特性的项目将具有类似的功能或实用性。因此，这些项目也可能是首选的用户。另一方面，对于两个候选项目的共同特征，其他用户可能给予其中一个正面的情绪，而给予另一个负面的情绪。显然，应该向用户推荐评价较高的项目。基于这两个动机，可以为每个候选项目建立相似度和情感评分的组合排序评分。

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review.<ref>{{cite book|first1=Yang|last1=Liu|first2=Xiangji|last2=Huang|first3=Aijun|last3=An|first4=Xiaohui|last4=Yu|chapter-url=http://www.yorku.ca/xhyu/papers/ICDM2008.pdf|chapter=Modeling and predicting the helpfulness of online reviews|year=2008|title=ICDM'08. Eighth IEEE international conference on Data mining|pages=443–452|publisher= IEEE|doi=10.1109/ICDM.2008.94|isbn=978-0-7695-3502-9|s2cid=18235238}}</ref> Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

除了情感分析本身的困难之外，对评论或反馈进行情感分析也面临着垃圾评论和有偏见的评论的挑战。其中一个工作方向是评估每个审查的有用性。写得不好的评论或反馈对推荐系统几乎没有任何帮助。此外，审查可能被设计成阻碍目标产品的销售，因此即使它写得很好也会对推荐系统产品造成伤害。

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,<ref>{{cite book|doi=10.1145/1871437.1871741|last1=Bermingham|first1=Adam|last2=Smeaton|first2=Alan F.|title=Classifying sentiment in microblogs: is brevity an advantage?|journal=Proceedings of the 19th ACM International Conference on Information and Knowledge Management|pages=1833|year=2010|isbn=9781450300995|s2cid=2084603|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref> because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

研究人员还发现，用户生成的长文本和短文本应该区别对待。一个有趣的结果表明，短形式的评论有时比长形式的评论更有帮助，因为它更容易过滤掉短形式文本中的干扰。对于长篇文本，文本长度的增长并不总是带来文本中特征或情感数量的相应增加。

Lamba & Madhusudhan<ref>{{cite journal |last1=Lamba |first1=Manika |last2=Madhusudhan |first2=Margam |title=Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity |journal=Social Network Analysis and Mining |year=2018 |volume=8 |issue=1|pages=1–12|doi=10.1007/s13278-018-0541-y |s2cid=53047128 }}</ref> introduce a nascent way to cater the information needs of today’s library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Lamba & Madhusudhan 介绍了一种新的方法来满足当今图书馆用户的信息需求，方法是将 Twitter 等社交媒体平台的情绪分析结果重新打包，以不同的格式提供综合的基于时间的服务。此外，他们还提出了一种利用社会媒体挖掘和情感分析在图书馆进行营销的新方法。

==See also==
* [[Emotion recognition]]
* [[Market sentiment]]
* [[Stylometry]]

* 情感识别
* 市场情绪
* 文体学

== References ==
{{Reflist|30em}}

{{Natural Language Processing}}

{{DEFAULTSORT:Sentiment Analysis}}
[[Category:Natural language processing]]
[[Category:Affective computing]]
[[Category:Social media]]
[[Category:Polling]]

Category:Natural language processing
Category:Affective computing
Category:Social media
Category:Polling

类别: 自然语言处理类别: 情感计算类别: 社会媒体类别: 轮询

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This page was moved from [[wikipedia:en:Sentiment analysis]]. Its edit history can be viewed at [[情感分析/edithistory]]</noinclude>

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