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

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dc.creatorTurney, Peter D.
dc.date2002-12-11
dc.date.accessioned2026-07-07T03:19:16Z
dc.date.available2026-07-07T03:19:16Z
dc.descriptionThis 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.
dc.description8 pages
dc.identifierhttps://arxiv.org/abs/cs/0212032
dc.identifierhttp://arxiv.org/abs/cs/0212032
dc.identifierProceedings of the 40th Annual Meeting of the Association for Computational Linguistics, (2002), Philadelphia, Pennsylvania, 417-424
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31391
dc.subjectMachine Learning
dc.subjectComputation and Language
dc.subjectInformation Retrieval
dc.subjectI.2.6; I.2.7; H.3.1; H.3.3
dc.titleThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
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