“情感分析”的版本间的差异

2021年8月11日 (三) 17:05的版本

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, 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 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 cruisers.
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 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 names, 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 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. ^[1]

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。^[1]

Precursors to sentimental analysis include the General Inquirer,^[2] 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.^[3]

情感分析的先驱包括 the General Inquirer，^[2] 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。^[3]

Subsequently, the method described in a patent by Volcani and Fogel,^[4] 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^[4] 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,^[5] and Pang^[6] who applied different methods for detecting the polarity of product reviews 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^[7] and Snyder^[8] among others: Pang and Lee^[7] 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^[8] 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^[5]和Pang^[6]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^[7]和Snyder^[8] 等人曾尝试这样做：Pang和Lee^[7]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^[8] 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.^[9]

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。^[9]

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 Max Entropy^[10] and SVMs^[11] 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 classifiers as implemented by the 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）^[10] 和支持向量机（SVMs）^[11] 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。^[12] 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.^[13]^[14]^[15]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.^[16]

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。^[13]^[14]^[15] 。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。^[16]

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.^[17] This problem can sometimes be more difficult than polarity classification.^[18] 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,^[19] results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang^[20] 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。^[17] 这个问题有时甚至比极性分类更加难以解决。^[18] 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^[19] 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^[20] 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.^[21]

客观指的是具有事实信息的事件。^[21]

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.^[22] 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).^[23] Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.^[23]

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）”。^[22] 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）。^[23] 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。^[23]

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.^[24]

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。^[24]

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).^[25] 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.^[26] At the moment, automated learning methods can further separate into supervised and 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。^[25] 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。^[26]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.^[27] 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.^[24]
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。^[27] 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。^[24]
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.^[21]

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.^[28]

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。^[28]

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.^[29] 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.^[30] 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）。^[29] 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）。^[30] 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.^[31]

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。^[31]

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.^[32]
Social media analysis.
Students' feedback classification.^[33]
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.^[34]
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。^[32]
社交媒体分析。
学生意见分类。^[33]
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。^[34]
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.^[35] 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.^[36] 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.^[37] The automatic identification of features can be performed with syntactic methods, with topic modeling,^[38]^[39] or with deep learning.^[40]^[41] More detailed discussions about this level of sentiment analysis can be found in Liu's work.^[23]

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^[35] 。“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。^[36] 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。^[37] 特征的自动识别可以通过语法方法、主题建模^[38]^[39] 或深度学习来实现。^[40]^[41] 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。^[23]

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.^[42] Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.^[43] Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.^[44] Statistical methods leverage elements from machine learning such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation,^[5] 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).^[45] To mine the opinion in 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.^[46] Hybrid approaches leverage both machine learning and elements from knowledge representation such as ontologies and semantic networks 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.^[47]

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）。^[42] 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。^[43] 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”。^[44] 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）。^[45] 语法依赖关系是通过对文本的深度解析得到的。^[46] 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。^[47]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.^[48] 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.^[49] 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^[50] 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,^[51] 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。^[48] 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。^[49] 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。^[50] 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，^[51] 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.^[52] However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.^[53]

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%^[54] 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.^[55]

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%^[54] 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。^[55]

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, jokes, 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.^[56]^[57]^[58]

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。^[56]^[57]^[58]

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 reputation.^[59]^[60]^[61]

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。^[59]^[60]^[61]

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

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 networks 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.^[62] Further complicating the matter, is the rise of anonymous social media platforms such as 4chan and Reddit.^[63] 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.^[64]

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。^[62] 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。^[63]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。^[64]

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 e-communities through sentiment analysis.^[65] The CyberEmotions project, for instance, recently identified the role of negative emotions in driving social networks discussions.^[66]

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。^[65] 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。^[66]

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.^[62] 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.^[67] Furthermore, sentiment analysis on Twitter has also been shown to capture the public mood behind human reproduction cycles on a planetary scale,^[68] as well as other problems of public-health relevance such as adverse drug reactions.^[69]

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。^[67] 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^[68] 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^[69]。

Application in recommender systems 推荐系统中的应用

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 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 services 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.^[70] 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。^[70] 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.^[71] 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,^[72] 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.^[71]

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。^[71] 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，^[72] 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。^[71]

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.^[73] 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，^[73]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,^[74] 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，^[74] 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^[75] 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^[75] 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。

References

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↑ ^8.0 ^8.1 ^8.2 ^8.3 Snyder, Benjamin; Barzilay, Regina (2007). "Multiple Aspect Ranking using the Good Grief Algorithm". Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL). pp. 300–307.
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↑ Ribeiro, Filipe Nunes; Araujo, Matheus (2010). "A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods". Transactions on Embedded Computing Systems. 9 (4).
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[:2-1] 1.0 ^1.1 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).

[:3-2] 2.0 ^2.1 Stone, Philip J., Dexter C. Dunphy, and Marshall S. Smith. "The general inquirer: A computer approach to content analysis." MIT Press, Cambridge, MA (1966).

[:4-3] 3.0 ^3.1 Gottschalk, Louis August, and Goldine C. Gleser. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press, 1969.

[:5-4] 4.0 ^4.1 模板:Cite patent

[Turney02-5] 5.0 ^5.1 ^5.2 Turney, Peter (2002). "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews". Proceedings of the Association for Computational Linguistics. pp. 417–424. arXiv:cs.LG/0212032.

[PangAl02-6] 6.0 ^6.1 Pang, Bo; Lee, Lillian; Vaithyanathan, Shivakumar (2002). "Thumbs up? Sentiment Classification using Machine Learning Techniques". Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 79–86.

[PangLee05-7] 7.0 ^7.1 ^7.2 ^7.3 Pang, Bo; Lee, Lillian (2005). "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales". Proceedings of the Association for Computational Linguistics (ACL). pp. 115–124.

[SnyderBarzilay07-8] 8.0 ^8.1 ^8.2 ^8.3 Snyder, Benjamin; Barzilay, Regina (2007). "Multiple Aspect Ranking using the Good Grief Algorithm". Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL). pp. 300–307.

[:6-9] 9.0 ^9.1 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.

[Vryniotis13-10] 10.0 ^10.1 Vryniotis, Vasilis (2013). The importance of Neutral Class in Sentiment Analysis.

[KoppelSchler06-11] 11.0 ^11.1 Koppel, Moshe; Schler, Jonathan (2006). "The Importance of Neutral Examples for Learning Sentiment". Computational Intelligence 22. pp. 100–109. CiteSeerX 10.1.1.84.9735.

[12] Ribeiro, Filipe Nunes; Araujo, Matheus (2010). "A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods". Transactions on Embedded Computing Systems. 9 (4).

[:7-13] 13.0 ^13.1 Taboada, Maite; Brooke, Julian (2011). "Lexicon-based methods for sentiment analysis". Computational Linguistics. 37 (2): 272–274. CiteSeerX 10.1.1.188.5517. doi:10.1162/coli_a_00049. S2CID 3181362.

[:8-14] 14.0 ^14.1 Augustyniak, Łukasz; Szymański, Piotr; Kajdanowicz, Tomasz; Tuligłowicz, Włodzimierz (2015-12-25). "Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis". Entropy (in English). 18 (1): 4. Bibcode:2015Entrp..18....4A. doi:10.3390/e18010004.

[:9-15] 15.0 ^15.1 Mehmood, Yasir; Balakrishnan, Vimala (2020-01-01). "An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration". Online Information Review. 44 (5): 1097–1117. doi:10.1108/OIR-10-2018-0295. ISSN 1468-4527.

[SentiStrength2010-16] 16.0 ^16.1 Thelwall, Mike; Buckley, Kevan; Paltoglou, Georgios; Cai, Di; Kappas, Arvid (2010). "Sentiment strength detection in short informal text". Journal of the American Society for Information Science and Technology. 61 (12): 2544–2558. CiteSeerX 10.1.1.278.3863. doi:10.1002/asi.21416.

[PangLee08Subjectivity-17] 17.0 ^17.1 Pang, Bo; Lee, Lillian (2008). "4.1.2 Subjectivity Detection and Opinion Identification". Opinion Mining and Sentiment Analysis. Now Publishers Inc. http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html.

[MihalceaAl07-18] 18.0 ^18.1 Mihalcea, Rada; Banea, Carmen; Wiebe, Janyce (2007). "Learning Multilingual Subjective Language via Cross-Lingual Projections" (PDF). Proceedings of the Association for Computational Linguistics (ACL). pp. 976–983. Archived from the original (PDF) on 2010-07-08.

[SuMarkert08-19] 19.0 ^19.1 Su, Fangzhong; Markert, Katja (2008). "From Words to Senses: a Case Study in Subjectivity Recognition" (PDF). Proceedings of Coling 2008, Manchester, UK.

[PangLee04-20] 20.0 ^20.1 Pang, Bo; Lee, Lillian (2004). "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts". Proceedings of the Association for Computational Linguistics (ACL). pp. 271–278.

[Wiebe_2005_486.E2.80.93497-21] 21.0 ^21.1 ^21.2 Wiebe, Janyce; Riloff, Ellen (2005). Gelbukh, Alexander (ed.). "Creating Subjective and Objective Sentence Classifiers from Unannotated Texts". Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science (in English). Berlin, Heidelberg: Springer. 3406: 486–497. doi:10.1007/978-3-540-30586-6_53. ISBN 978-3-540-30586-6.

[:10-22] 22.0 ^22.1 Quirk, Randolph; Greenbaum, Sidney; Geoffrey, Leech; Jan, Svartvik (1985). A Comprehensive Grammar of the English Language (General Grammar). Longman. pp. 175–239. ISBN 1933108312.

[Liu2010-23] 23.0 ^23.1 ^23.2 ^23.3 ^23.4 ^23.5 Liu, Bing (2010). "Sentiment Analysis and Subjectivity" (PDF). In Indurkhya, N.; Damerau, F. J. (eds.). Handbook of Natural Language Processing (Second ed.).

[:1-24] 24.0 ^24.1 ^24.2 ^24.3 Pang, Bo; Lee, Lillian (2008-07-06). "Opinion Mining and Sentiment Analysis". Foundations and Trends in Information Retrieval (in English). 2 (1–2): 1–135. doi:10.1561/1500000011. ISSN 1554-0669.

[:11-25] 25.0 ^25.1 Riloff, Ellen; Wiebe, Janyce (2003-07-11). "Learning extraction patterns for subjective expressions". Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. EMNLP '03. USA: Association for Computational Linguistics. 10: 105–112. doi:10.3115/1119355.1119369. S2CID 6541910.

[:12-26] 26.0 ^26.1 Chaturvedi, Iti; Cambria, Erik; Welsch, Roy E.; Herrera, Francisco (November 2018). "Distinguishing between facts and opinions for sentiment analysis: Survey and challenges" (PDF). Information Fusion. 44: 65–77. doi:10.1016/j.inffus.2017.12.006 – via Elsevier Science Direct.

[:13-27] 27.0 ^27.1 Wiebe, Janyce; Riloff, Ellen (July 2011). "Finding Mutual Benefit between Subjectivity Analysis and Information Extraction". IEEE Transactions on Affective Computing. 2 (4): 175–191. doi:10.1109/T-AFFC.2011.19. ISSN 1949-3045. S2CID 16820846.

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