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

  • Authors:

  • Peter D. Turney

  • Affiliations:

  • National Research Council of Canada, Ottawa, Ontario, Canada

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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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.