Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

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TypeArticle
ConferenceProceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL'02), July 8-10, 2002., Philadelphia, Pennsylvania, USA
AbstractThis 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number44946
NPARC number8914166
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Record identifier4bb7a0c8-9d9b-4ded-bcf6-fdf64ee28ccc
Record created2009-04-22
Record modified2016-05-09
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