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

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Peter D. TurneyAuthors Info & Claims

ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics

Pages 417 - 424

https://doi.org/10.3115/1073083.1073153

Published: 06 July 2002 Publication History

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Abstract

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.

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ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics

July 2002

543 pages

General Chair:
Pierre Isabelle

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Association for Computational Linguistics

United States

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Published: 06 July 2002

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Thumbs up?: sentiment classification using machine learning techniques

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