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第426行： − + − [[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.+ − + − [[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. − − [[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. − + − + − + 第451行：
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无编辑摘要
</ref>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。
</ref>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。
客观指的是具有事实信息的事件。<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">{{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}}</ref>
* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”
* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”
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">{{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>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" />
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">{{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>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" />
* 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。
* 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">{{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}}</ref>
* 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">{{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}}</ref>
* 社交媒体分析。
* 社交媒体分析。
* 学生意见分类。<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>
* 学生意见分类。<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}}</ref>
* 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。
* 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">{{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>
* 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">{{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>
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
| url = http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
}}
}}
</ref> 特征的自动识别可以通过语法方法、主题建模<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}}</ref>或深度学习来实现。<ref name="Poria">{{cite journal
</ref> 特征的自动识别可以通过语法方法、主题建模<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|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|first1 = Ivan|last1 = Titov|first2 = Ryan|last2 = McDonald|arxiv = 0801.1063}}</ref>或深度学习来实现。<ref name="Poria">{{cite journal
| first = Soujanya | last = Poria | display-authors=etal
| first = Soujanya | last = Poria | display-authors=etal
| title = Aspect extraction for opinion mining with a deep convolutional neural network
| title = Aspect extraction for opinion mining with a deep convolutional neural network
</ref>
</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">
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">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">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=":29">
</ref><ref name=":30">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><ref name=":30">
</ref>
</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">
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">
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.
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">
</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.
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">
</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.
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>
</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">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>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">{{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>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">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">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>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">{{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>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">Kirkpatrick, Marshall. [https://readwrite.com/2009/04/15/whats_next_in_social_media_monitoring/ "], ''ReadWriteWeb'', 2009-04-15. Retrieved on 2009-10-01.</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">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>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">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">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>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">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>
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">{{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}}</ref>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。
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">{{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|isbn=978-0-7695-3502-9}}</ref>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。
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">{{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|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref>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。
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">{{cite book|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|url=http://doras.dcu.ie/15663/1/cikm1079-bermingham.pdf}}</ref>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。