Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews

Turney, Peter D. (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. [Conference Paper]

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This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

Item Type:Conference Paper
Subjects:Computer Science > Artificial Intelligence
Computer Science > Language
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:2321
Deposited By:Turney, Peter
Deposited On:15 Jul 2002
Last Modified:11 Mar 2011 08:54

References in Article

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Agresti, A. 1996. An introduction to categorical data analysis. New York: Wiley.

Brill, E. 1994. Some advances in transformation-based part of speech tagging. Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 722-727). Menlo Park, CA: AAAI Press.

Church, K.W., & Hanks, P. 1989. Word association norms, mutual information and lexicography. Proceedings of the 27th Annual Conference of the ACL (pp. 76-83). New Brunswick, NJ: ACL.

Frank, E., & Hall, M. 2001. A simple approach to ordinal classification. Proceedings of the Twelfth European Conference on Machine Learning (pp. 145-156). Berlin: Springer-Verlag.

Hatzivassiloglou, V., & McKeown, K.R. 1997. Predicting the semantic orientation of adjectives. Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the European Chapter of the ACL (pp. 174-181). New Brunswick, NJ: ACL.

Hatzivassiloglou, V., & Wiebe, J.M. 2000. Effects of adjective orientation and gradability on sentence subjectivity. Proceedings of 18th International Conference on Computational Linguistics. New Brunswick, NJ: ACL.

Hearst, M.A. 1992. Direction-based text interpretation as an information access refinement. In P. Jacobs (Ed.), Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval. Mahwah, NJ: Lawrence Erlbaum Associates.

Landauer, T.K., & Dumais, S.T. 1997. A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.

Santorini, B. 1995. Part-of-Speech Tagging Guidelines for the Penn Treebank Project (3rd revision, 2nd printing). Technical Report, Department of Computer and Information Science, University of Pennsylvania.

Spertus, E. 1997. Smokey: Automatic recognition of hostile messages. Proceedings of the Conference on Innovative Applications of Artificial Intelligence (pp. 1058-1065). Menlo Park, CA: AAAI Press.

Tong, R.M. 2001. An operational system for detecting and tracking opinions in on-line discussions. Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification (pp. 1-6). New York, NY: ACM.

Turney, P.D. 2001. Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. Proceedings of the Twelfth European Conference on Machine Learning (pp. 491-502). Berlin: Springer-Verlag.

Wiebe, J.M. 2000. Learning subjective adjectives from corpora. Proceedings of the 17th National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press.

Wiebe, J.M., Bruce, R., Bell, M., Martin, M., & Wilson, T. 2001. A corpus study of evaluative and speculative language. Proceedings of the Second ACL SIG on Dialogue Workshop on Discourse and Dialogue. Aalborg, Denmark.


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