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Opinion Mining

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Encyclopedia of Database Systems

Synonyms

Sentiment analysis

Definition

Given a set of evaluative text documents D that contain opinions (or sentiments) about an object, opinion mining aims to extract attributes and components of the object that have been commented on in each document d ∈ D and to determine whether the comments are positive, negative or neutral.

Historical Background

Textual information in the world can be broadly classified into two main categories, facts and opinions. Facts are objective statements about entities and events in the world. Opinions are subjective statements that reflect people’s sentiments or perceptions about the entities and events. Much of the existing research on text information processing has been (almost exclusively) focused on mining and retrieval of factual information, e.g., information retrieval, Web search, and many other text mining and natural language processing tasks. Little work has been done on the processing of opinions until only recently. Yet, opinions are so...

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Recommended Reading

  1. Carenini G., Ng R., and Zwart E. Extracting Knowledge from Evaluative Text. In Proc. 3rd Int. Conf. on Knowledge Capture, 2005.

    Google Scholar 

  2. Dave D., Lawrence A., and Pennock D. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proc. 12th Int. World Wide Web Conference, 2003.

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  3. Ding X., Liu B., and Yu P. A holistic lexicon-based approach to opinion mining. In Proc. 1st ACM Int. Conf. on Web Search and Data Mining. 2008.

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  4. Ganapathibhotla G. and Liu B. Identifying preferred entities in comparative sentences. In Proc. 22nd Int. Conf. on Computational Linguistics. 2008.

    Google Scholar 

  5. Hatzivassiloglou V. and McKeown K. Predicting the semantic orientation of adjectives. In Proc. 8th Conf. European Chapter of Assoc. Comp. Linguistics. 1997.

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  6. Hu M. and Liu B. Mining and summarizing customer reviews. In Proc. 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2004.

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  7. Jindal N. and Liu B. Mining comparative sentences and relations. In Proc. of National Conf. on Artificial Intelligence, 2006.

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  8. Kanayama H. and Nasukawa T. Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proc. 2006 Conf. on Empirical Methods in Natural Language Processing, 2006.

    Google Scholar 

  9. Kim S. and Hovy E. Determining the sentiment of opinions. In Proc. 20th Int. Conf. on Computational Linguistics, 2004.

    Google Scholar 

  10. Liu B., Hu M., and Cheng J. Opinion observer: analyzing and comparing opinions on the web. In Proc. 14th Int. World Wide Web Conference, 2005.

    Google Scholar 

  11. Pang B., Lee L., and Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In Proc. 2002 Conf. on Empirical Methods in Natural Language Processing, 2002.

    Google Scholar 

  12. Popescu A.-M. and Etzioni O. Extracting product features and opinions from reviews. In Proc. 2005 Conf. on Empirical Methods in Natural Language Processing, 2005.

    Google Scholar 

  13. Turney P. Thumbs up or thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proc. 40th Annual Mfg. of Assoc. Comp. Linguistics, 2002.

    Google Scholar 

  14. Wiebe J. and Riloff E. Creating subjective and objective sentence classifiers from unannotated texts. In Proc. Int. Conf. on Intelligent Text Processing and Computational Linguistics, 2005.

    Google Scholar 

  15. Wilson T., Wiebe J., and Hwa R. Just how mad are you? Finding strong and weak opinion clauses. In Proc. National Conf. on Artificial Intelligence, 2004.

    Google Scholar 

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Author information

Authors and Affiliations

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

    Bing Liu

Authors
  1. Bing Liu
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Liu, B. (2009). Opinion Mining. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_257

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