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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>

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。例如，高级的“超极性”情感分类研究关注的是情绪状态，如享受、愤怒、厌恶、悲伤、恐惧和惊讶。<ref name=":2" />

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>

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>

}}</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.

}}</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。

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">

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">

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)。

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>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>

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>

2004年美国科学促进会春季研讨会上，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，用于对文本 qu、 Yan、 James shanawe 和 janice wice 的影响、吸引力、主观性和情感的系统计算研究。探索文本中的态度和情感: 理论和应用在 AAAI 春季研讨会)技术报告 SS-04-07。美国科学促进协会出版社，门洛帕克。2004.

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">

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">

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</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.<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> 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.

</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.

尽管在大多数分类分类方法中，中性类在假设二进制分类器的边界附近被忽略，一些研究人员建议，在每个极性问题中，必须识别3个类别。此外，还证明了引入中性分类器可以有效地提高分类器的整体准确率，如最大熵和支持向量机。原则上有两种操作中立类的方法。要么，算法首先识别中性语言，过滤掉它，然后根据积极和消极情绪评估其余的语言，要么在一个步骤中建立一个三向分类。第二种方法通常涉及到对所有类别的概率分布进行估计(例如:。NLTK 实现的朴素贝叶斯分类器)。是否以及如何使用中性类取决于数据的性质: 如果数据被清晰地分类为中性、消极和积极的语言，那么过滤掉中性语言并关注积极和消极情绪之间的极性是有意义的。相比之下，如果数据大部分是中性的，对积极和消极影响的偏差很小，这种策略将使清楚地区分两极变得更加困难。

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">

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">

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