Paper: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

ACL ID P02-1053
Title Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not rec- ommended (thumbs down). The classifi- cation of a review is predicted by the average semantic orientation of the phrases in the review that contain adjec- tives or adverbs. A phrase has a positive semantic orientation when it has good as- sociations (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 infor- mation 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 ori- entatio...