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A Survey of Opinion Mining and Sentiment Analysis

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Abstract

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product.

With the explosive growth of social media (i.e., reviews, forum discussions, blogs and social networks) on the Web, individuals and organizations are increasingly using public opinions in these media for their decision making. However, finding and monitoring opinion sites on the Web and distilling the information contained in them remains a formidable task because of the proliferation of diverse sites. Each site typically contains a huge volume of opinionated text that is not always easily deciphered in long forum postings and blogs. The average human reader will have difficulty identifying relevant sites and accurately summarizing the information and opinions contained in them. Moreover, it is also known that human analysis of text information is subject to considerable biases, e.g., people often pay greater attention to opinions that are consistent with their own preferences. People also have difficulty, owing to their mental and physical limitations, producing consistent results when the amount of information to be processed is large. Automated opinion mining and summarization systems are thus needed, as subjective biases and mental limitations can be overcome with an objective sentiment analysis system.

In the past decade, a considerable amount of research has been done in academia [58,76]. There are also numerous commercial companies that provide opinion mining services. In this chapter, we first define the opinion mining problem. From the definition, we will see the key technical issues that need to be addressed. We then describe various key mining tasks that have been studied in the research literature and their representative techniques. After that, we discuss the issue of detecting opinion spam or fake reviews. Finally, we also introduce the research topic of assessing the utility or quality of online reviews.

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References

  1. Andreevskaia, A. and S. Bergler. Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses. In Proceedings of Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), 2006.

    Google Scholar 

  2. Aue, A. and M. Gamon. Customizing sentiment classifiers to new domains: a case study. In Proceedings of Recent Advances in Natural Language Processing (RANLP-2005), 2005.

    Google Scholar 

  3. Banea, C., R. Mihalcea and J. Wiebe. Multilingual subjectivity: are more languages better?. In Proceedings of International Conference on Computational Linguistics (COLING-2010), 2010.

    Google Scholar 

  4. Beineke, P., T. Hastie, C. Manning, and S. Vaithyanathan. An exploration of sentiment summarization. In Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, 2003.

    Google Scholar 

  5. Bethard, S., H. Yu, A. Thornton, V. Hatzivassiloglou, and D. Jurafsky. Automatic extraction of opinion propositions and their holders. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, 2004.

    Google Scholar 

  6. Blei, D., A. Ng, and M. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3: p. 993-1022.

    MATH  Google Scholar 

  7. Blitzer, J., M. Dredze, and F. Pereira. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2007), 2007.

    Google Scholar 

  8. Boyd-Graber, J. and P. Resnik. Holistic sentiment analysis across languages: multilingual supervised latent dirichlet allocation. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-2010), 2010.

    Google Scholar 

  9. Breck, E., Y. Choi, and C. Cardie. Identifying expressions of opinion in context. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2007), 2007.

    Google Scholar 

  10. Brody, S. and S. Elhadad. An unsupervised aspect-sentiment model for online reviews. In Proceedings of The 2010 Annual Conference of the North American Chapter of the ACL, 2010.

    Google Scholar 

  11. Carenini, G., R. Ng, and A. Pauls. Multi-document summarization of evaluative text. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL-2006), 2006.

    Google Scholar 

  12. Carenini, G., R. Ng, and E. Zwart. Extracting knowledge from evaluative text. In Proceedings of Third Intl. Conf. on Knowledge Capture (K-CAP-05), 2005.

    Google Scholar 

  13. Choi, Y., C. Cardie, E. Riloff, and S. Patwardhan. Identifying sources of opinions with conditional random fields and extraction patterns. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-2005), 2005.

    Google Scholar 

  14. Choi, Y. and C. Claire. Adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), 2009.

    Google Scholar 

  15. Dave, K., S. Lawrence, and D. Pennock. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of International Conference on World Wide Web (WWW-2003), 2003.

    Google Scholar 

  16. Ding, X. and B. Liu. Resolving object and attribute coreference in opinion mining. In Proceedings of International Conference on Computational Linguistics (COLING-2010), 2010.

    Google Scholar 

  17. Ding, X., B. Liu, and P. Yu. A holistic lexicon-based approach to opinion mining. In Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2008), 2008.

    Google Scholar 

  18. Ding, X., B. Liu, and L. Zhang. Entity discovery and assignment for opinion mining applications. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009), 2009.

    Google Scholar 

  19. Esuli, A. and F. Sebastiani. Determining term subjectivity and term orientation for opinion mining. In Proceedings of Conf. of the European Chapter of the Association for Computational Linguistics (EACL-2006), 2006.

    Google Scholar 

  20. Esuli, A. and F. Sebastiani. Determining the semantic orientation of terms through gloss classification. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2005), 2005.

    Google Scholar 

  21. Esuli, A. and F. Sebastiani. SentiWordNet: a publicly available lexical resource for opinion mining. In Proceedings of Language Resources and Evaluation (LREC-2006), 2006.

    Google Scholar 

  22. Freitag, D. and A. McCallum. Information extraction with HMM structures learned by stochastic optimization. In Proceedings of National Conf. on Artificial Intelligence (AAAI-2000), 2000.

    Google Scholar 

  23. Gamon, M., A. Aue, S. Corston-Oliver, and E. Ringger. Pulse: Mining customer opinions from free text. Advances in Intelligent Data Analysis VI, 2005: p. 121-132.

    Google Scholar 

  24. Ganapathibhotla, M. and B. Liu. Mining opinions in comparative sentences. In Proceedings of International Conference on Computational Linguistics (COLING-2008), 2008.

    Google Scholar 

  25. Ghahramani, Z. and K. Heller. Bayesian sets. Advances in Neural Information Processing Systems (NIPS-2005), 2005.

    Google Scholar 

  26. Ghose, A. and P. Ipeirotis. Designing novel review ranking systems: predicting the usefulness and impact of reviews. In Proceedings of the International Conference on Electronic Commerce, 2007.

    Google Scholar 

  27. Guo, H., H. Zhu., X. Zhang., and Z. Su. OpinionIt: a text mining system for cross-lingual opinion analysis. In Proceedings of ACM International Conference on Information and knowledge management (CIKM-2010), 2010.

    Google Scholar 

  28. Hassan, A., V. Qazvinian., D. Radev. What’s with the attitude? identifying sentences with attitude in online discussion. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 2010.

    Google Scholar 

  29. Hatzivassiloglou, V. and K. McKeown. Predicting the semantic orientation of adjectives. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-1997), 1997.

    Google Scholar 

  30. Hatzivassiloglou, V. and J. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of International Conference on Computational Linguistics (COLING-2000), 2000.

    Google Scholar 

  31. Hu, M. and B. Liu. Mining and summarizing customer reviews. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), 2004.

    Google Scholar 

  32. Huang, X. and W. B. Croft. A unified relevance model for opinion retrieval. In Proceedings of ACM International Conference on Information and knowledge management (CIKM-2009), 2009.

    Google Scholar 

  33. Jakob, N. and I. Gurevych. Extracting opinion targets in a singleand cross-domain setting with conditional random fields. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 2010.

    Google Scholar 

  34. Jin, W. and H. Ho. A novel lexicalized HMM-based learning framework for web opinion mining. In Proceedings of International Conference on Machine Learning (ICML-2009), 2009.

    Google Scholar 

  35. Jin, W. and H. Ho. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009), 2009.

    Google Scholar 

  36. Jindal, N. and B. Liu. Identifying comparative sentences in text documents. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2006), 2006.

    Google Scholar 

  37. Jindal, N. and B. Liu. Mining comparative sentences and relations. In Proceedings of National Conf. on Artificial Intelligence (AAAI-2006), 2006.

    Google Scholar 

  38. Jindal, N. and B. Liu. Opinion spam and analysis. In Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2008), 2008.

    Google Scholar 

  39. Jindal, N. and B. Liu. Review spam detection. In Proceedings of International Conference on World Wide Web (WWW-2007), 2007.

    Google Scholar 

  40. Jindal, N., B. Liu, and E. Lim. Finding unusual review patterns using unexpected rules. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2010), 2010.

    Google Scholar 

  41. Jo, Y. and A. Oh. Aspect and sentiment unification model for online review analysis. In Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2011), 2011.

    Google Scholar 

  42. Joachims, T. Optimizing search engines using clickthrough data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD-2002), 2002.

    Google Scholar 

  43. Kaji, N. and M. Kitsuregawa. Automatic construction of polaritytagged corpus from HTML documents. In Proceedings of COLING/ ACL 2006 Main Conference Poster Sessions (COLING-ACL-2006), 2006.

    Google Scholar 

  44. Kaji, N. and M. Kitsuregawa. Building lexicon for sentiment analysis from massive collection of HTML documents. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-2007), 2007.

    Google Scholar 

  45. Kamps, J., M. Marx, R. Mokken, and M. De Rijke. Using WordNet to measure semantic orientation of adjectives. In Proc. of LREC-2004, 2004.

    Google Scholar 

  46. Kanayama, H. and T. Nasukawa. Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2006), 2006.

    Google Scholar 

  47. Kim, J., J. Li., and J. Lee. Evaluating multilanguage-comparability of subjective analysis system. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  48. Kim, S. and E. Hovy. Crystal: analyzing predictive opinions on the web. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL-2007), 2007.

    Google Scholar 

  49. Kim, S. and E. Hovy. Determining the sentiment of opinions. In Proceedings of Interntional Conference on Computational Linguistics (COLING-2004), 2004.

    Google Scholar 

  50. Kim, S., P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2006), 2006.

    Google Scholar 

  51. Ku, L., Y. Liang, and H. Chen. Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAICAAW-2006, 2006.

    Google Scholar 

  52. Lafferty, J., A. McCallum, and F. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of International Conference on Machine Learning (ICML-2001), 2001.

    Google Scholar 

  53. Lee, L. Measures of distributional similarity. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-1999), 1999.

    Google Scholar 

  54. Li, S., C. Lin, Y. Song, and Z. Li. Comparable entity mining from comparative questions. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  55. Li, X., L. Zhang, B. Liu, and S. Ng. Distributional similarity vs. PU learning for entity set expansion. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  56. Lim, E., V. Nguyen, N. Jindal, B. Liu, and H. Lauw. Detecting product review spammers using rating behaviors. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2010), 2010.

    Google Scholar 

  57. Lin, C. and Y. He. Joint sentiment/topic model for sentiment analysis. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2009), 2009.

    Google Scholar 

  58. Liu, B. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition, N. Indurkhya and F.J. Damerau, Editors. 2010.

    Google Scholar 

  59. Liu, B. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Second Edition, Springer, 2011.

    Google Scholar 

  60. Liu, B., M. Hu, and J. Cheng. Opinion observer: analyzing and comparing opinions on the web. In Proceedings of International Conference on World Wide Web (WWW-2005), 2005.

    Google Scholar 

  61. Liu, J., Y. Cao, C. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLPCoNLL-2007), 2007.

    Google Scholar 

  62. Liu, Y., X. Huang, A. An, and X. Yu. ARSA: a sentiment-aware model for predicting sales performance using blogs. In Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2007), 2007.

    Google Scholar 

  63. Lu, Y., M. Castellanos, U. Dayal, and C. Zhai. Automatic construction of a context-aware sentiment lexicon: an optimization approach. In Proceedings of International Conference on World Wide Web (WWW-2011), 2011.

    Google Scholar 

  64. Lu, Y., P. Tsaparas., A. Ntoulas., and L. Polanyi. Exploiting social context for review quality prediction. In Proceedings of International Conference on World Wide Web (WWW-2010), 2010.

    Google Scholar 

  65. Lu, Y. and C. Zhai. Opinion integration through semi-supervised topic modeling. In Proceedings of International Conference on World Wide Web (WWW-2008), 2008.

    Google Scholar 

  66. Lu, Y., C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In Proceedings of International Conference on World Wide Web (WWW-2009), 2009.

    Google Scholar 

  67. Mei, Q., X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: modeling facets and opinions in weblogs. In Proceedings of International Conference on World Wide Web (WWW-2007), 2007.

    Google Scholar 

  68. Melville, P., W. Gryc., R. D. Lawrence. Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009), 2009.

    Google Scholar 

  69. Miller, G., R. Beckwith, C. Fellbaum, D. Gross, and K. Miller. WordNet: an on-line lexical database. Oxford Univ. Press., 1990.

    Google Scholar 

  70. Mishne, G. and N. Glance. Predicting movie sales from blogger sentiment. In Proceedings of AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW-2006), 2006.

    Google Scholar 

  71. Mohammad, S., C. Dunne., and B. Dorr. Generating high-coverage semantic orientation lexicons from overly marked words and a thesaurus. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), 2009.

    Google Scholar 

  72. Mukherjee, A., B. Liu, J.Wang, N. Glance, and N. Jindal. Detecting group review spam. In Proceedings of International Conference on World Wide Web (WWW-2011), 2011.

    Google Scholar 

  73. Nishikawa, H., T. Hasegawa, Y. Matsuo, and G. Kikui. Optimizing informativeness and readability for sentiment summarization. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  74. Paltoglou, G. and M. Thelwall. A study of information retrieval weighting schemes for sentiment analysis. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  75. Pan, S., X. Ni, J. Sun, Q. Yang, and Z. Chen. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of International Conference on World Wide Web (WWW-2010), 2010.

    Google Scholar 

  76. Pang, B. and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2): p. 1-135, 2008.

    Article  Google Scholar 

  77. Pang, B. and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of Meeting of the Association for Computational Linguistics (ACL-2005), 2005.

    Google Scholar 

  78. Pang, B., L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2002), 2002.

    Google Scholar 

  79. Pantel, P., E. Crestan, A. Borkovsky, A. Popescu, and V. Vyas. Web-scale distributional similarity and entity set expansion. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), 2009.

    Google Scholar 

  80. Parrott, W. Emotions in social psychology: Essential readings. Psychology Press, 2001.

    Google Scholar 

  81. Paul, M., C. Zhai, and R. Girju. Summarizing contrastive viewpoints in opinionated text. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 2010.

    Google Scholar 

  82. Polanyi, L. and A. Zaenen. Contextual valence shifters. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, 2004.

    Google Scholar 

  83. Popescu, A. and O. Etzioni. Extracting product features and opinions from reviews. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2005), 2005.

    Google Scholar 

  84. Qiu, G., B. Liu, J. Bu, and C. Chen. Expanding domain sentiment lexicon through double propagation. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-2009), 2009.

    Google Scholar 

  85. Qiu, G., B. Liu, J. Bu, and C. Chen. Opinion word expansion and target extraction through double propagation. Computational Linguistics, 2011.

    Google Scholar 

  86. Qiu, L., W. Zhang., C. Hu., and K. Zhao. SELC: A self-supervised model for sentiment classification. In Proceedings of ACM International Conference on Information and knowledge management (CIKM-2009), 2009.

    Google Scholar 

  87. Riloff, E., S. Patwardhan, and J. Wiebe. Feature subsumption for opinion analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2006), 2006.

    Google Scholar 

  88. Riloff, E. and J. Wiebe. Learning extraction patterns for subjective expressions. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2003), 2003.

    Google Scholar 

  89. Seki, Y., K. Eguchi, N. Kando, and M. Aono. Opinion-focused summarization and its analysis at DUC 2006. In Proceedings of the Document Understanding Conference (DUC), 2006.

    Google Scholar 

  90. Stone, P. The general inquirer: a computer approach to content analysis. Journal of Regional Science, 8(1), 1968.

    Google Scholar 

  91. Stoyanov, V. and C. Cardie. Partially supervised coreference resolution for opinion summarization through structured rule learning. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2006), 2006.

    Google Scholar 

  92. Su, Q., X. Xu, H. Guo, Z. Guo, X. Wu, X. Zhang, B. Swen, and Z. Su. Hidden sentiment association in chinese web opinion mining. In Proceedings of International Conference on World Wide Web (WWW-2008), 2008.

    Google Scholar 

  93. Taboada, M., J, Brooke, M. Tofiloski, K. Voll, and M. Stede, Lexicon-based methods for sentiment analysis. Computational Intelligence, 2010.

    Google Scholar 

  94. Takamura, H., T. Inui, and M. Okumura. Extracting semantic orientations of phrases from dictionary. In Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL-2007), 2007.

    Google Scholar 

  95. Tan, S., Y. Wang., and X. Cheng. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), 2008.

    Google Scholar 

  96. Tata, S. and B. Di Eugenio. Generating fine-grained reviews of songs from album reviews. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010.

    Google Scholar 

  97. Tesniere, L. Elements de syntaxe structurale: Pref. de Jean Fourquet. 1959: C. Klincksieck.

    Google Scholar 

  98. Titov, I. and R. McDonald. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2008), 2008.

    Google Scholar 

  99. Titov, I. and R. McDonald. Modeling online reviews with multigrain topic models. In Proceedings of International Conference on World Wide Web (WWW-2008), 2008.

    Google Scholar 

  100. Turney, P. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2002), 2002.

    Google Scholar 

  101. Wan, X. Co-training for cross-lingual sentiment classification. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2009), 2009.

    Google Scholar 

  102. Wang, H., Y. Lu, and C. Zhai. Latent aspect rating analysis on review text data: a rating regression approach. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010), 2010.

    Google Scholar 

  103. Wiebe, J. Learning subjective adjectives from corpora. In Proceedings of National Conf. on Artificial Intelligence (AAAI-2000), 2000.

    Google Scholar 

  104. Wiebe, J., R. Bruce, and T. O’Hara. Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the Association for Computational Linguistics (ACL-1999), 1999.

    Google Scholar 

  105. Wiebe, J. and E. Riloff. Creating subjective and objective sentence classifiers from unannotated texts. Computational Linguistics and Intelligent Text Processing, p. 486-497, 2005.

    Google Scholar 

  106. Wiebe, J., T. Wilson, R. Bruce, M. Bell, and M. Martin. Learning subjective language. Computational Linguistics, 30(3): p. 277-308, 2004.

    Article  Google Scholar 

  107. Wiebe, J., T. Wilson, and C. Cardie. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2): p. 165-210, 2005.

    Article  Google Scholar 

  108. Wilson, T., J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-2005), 2005.

    Google Scholar 

  109. Wilson, T., J. Wiebe, and R. Hwa. Just how mad are you? finding strong and weak opinion clauses. In Proceedings of National Conference on Artificial Intelligence (AAAI-2004), 2004.

    Google Scholar 

  110. Wilson, T., J. Wiebe, and R. Hwa. Recognizing strong and weak opinion clauses. Computational Intelligence, 22(2): p. 73-99, 2006.

    Article  MathSciNet  Google Scholar 

  111. Wu, Y., Q. Zhang, X. Huang, and L. Wu. Phrase dependency parsing for opinion mining. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), 2009.

    Google Scholar 

  112. Yang, H., L. Si, and J. Callan. Knowledge transfer and opinion detection in the TREC2006 blog track. In Proceedings of TREC, 2006.

    Google Scholar 

  113. Yu, H. and V. Hatzivassiloglou. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2003), 2003.

    Google Scholar 

  114. Zhai, Z., B. Liu, H. Xu, and P. Jia. Grouping product features using semi-supervised learning with soft-constraints. In Proceedings of International Conference on Computational Linguistics (COLING-2010), 2010.

    Google Scholar 

  115. Zhai, Z., B. Liu., L. Zhang., H. Xu., and P. Jia. Identifying evaluative sentences in online discussions. In Proceedings of National Conf. on Artificial Intelligence (AAAI-2011), 2011.

    Google Scholar 

  116. Zhang, L. and B. Liu. Identifying noun product features that imply opinions. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2011), 2011.

    Google Scholar 

  117. Zhang, M. and X. Ye. A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), 2008.

    Google Scholar 

  118. Zhang, Z. and B. Varadarajan. Utility scoring of product reviews. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006.

    Google Scholar 

  119. Zhao, W., J. Jiang, H. Yan, and X. Li. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 2010.

    Google Scholar 

  120. Zhuang, L., F. Jing, and X. Zhu. Movie review mining and summarization. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006.

    Google Scholar 

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  1. University of Illinois at Chicago, Chicago, IL, USA

    Bing Liu & Lei Zhang

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    Charu C. Aggarwal

  2. at Urbana-Champaign, University of Illinois, URBANA, 61801, Illinois, USA

    ChengXiang Zhai

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Liu, B., Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In: Aggarwal, C., Zhai, C. (eds) Mining Text Data. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3223-4_13

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