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.

Citations

817	Categorical Data Analysis - Agresti - 2002
764	S.: A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104 - Landauer, Dumais - 1997
627	Word association norms, mutual information, and lexicography - Church, Hanks - 1990
227	Some advances in transformation-based part of speech tagging - Brill
200	Predicting the semantic orientation of adjectives - Hatzivassiloglou, McKeown - 1997
128	Part-of-speech Tagging Guidelines for the Penn Treebank Project - Santorini - 1990
68	Effects of adjective orientation and gradability on sentence subjectivity - Hatzivassiloglou, Wiebe - 2000
48	Smokey: “Automatic Recognition of Hostile Messages - Spertus - 2000
39	Direction-Based text interpretation as an information access refinement - HEARST - 1992
35	M.: A Simple Approach to Ordinal Classification - Frank, Hall - 2001