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.
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...
- 1.Carenini G, Ng R, Zwart E. Extracting knowledge from evaluative text. In: Proceedings of the 3rd International Conference on Knowledge Capture; 2005.Google Scholar
- 2.Dave D, Lawrence A, Pennock D. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International World Wide Web Conference; 2003.Google Scholar
- 3.Ding X, Liu B, Yu P. A holistic lexicon-based approach to opinion mining. In: Proceedings of the 1st ACM International Conference on Web Search and Data Mining; 2008.Google Scholar
- 4.Ganapathibhotla G, Liu B. Identifying preferred entities in comparative sentences. In: Proceedings of the 22nd International Conference on Computational Linguistics; 2008.Google Scholar
- 5.Hatzivassiloglou V, McKeown K. Predicting the semantic orientation of adjectives. In: Proceedings of the 8th Conference of the European Chapter of the Association Computational Linguistics; 1997.Google Scholar
- 6.Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004.Google Scholar
- 7.Jindal N, Liu B. Mining comparative sentences and relations. In: Proceedings of the 21st National Conference on Artificial Intelligence and 18th Innovative Applications of Artificial Intelligence Conference; 2006.Google Scholar
- 8.Kanayama H, Nasukawa T. Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing; 2006.Google Scholar
- 9.Kim S, Hovy E. Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics; 2004.Google Scholar
- 10.Liu B, Hu M, Cheng J. Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International World Wide Web Conference; 2005.Google Scholar
- 11.Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing; 2002.Google Scholar
- 12.Popescu A-M, Etzioni O. Extracting product features and opinions from reviews. In: Proceedings of the 2005 Conference 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: Proceedings of the 40th Annual Manufacturing of Association Computational Linguistics; 2002.Google Scholar
- 15.Wilson T, Wiebe J, Hwa R. Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of the 19th National Conference on Artificial Intelligence and 16th Innovative Applications of Artificial Intelligence Conference; 2004.Google Scholar