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Cited by 2,352

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

Open access
Turney, Peter D.·2001·Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02

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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Metadata

Authors
Turney, P. D.
Venue
Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02
Published
2001
Pages
417
OpenAlex
W193780173
Cited by
2,352
References
1
FWCI
6.70
Type
conference paper
Language
en
Open access
bronze

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