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.
Keywords
PhraseOrientation (vector space)Natural language processingWord (group theory)Computer scienceArtificial intelligencePattern recognition (psychology)LinguisticsMathematics
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 417-424
- Citations
- 3653
- Access
- Closed
External Links
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Cite This
Peter Peter,
Turney
(2002).
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews.
Meeting of the Association for Computational Linguistics
, 417-424.