Paper: Extracting Product Features And Opinions From Reviews

ACL ID H05-1043
Title Extracting Product Features And Opinions From Reviews
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005

Consumers are often forced to wade through many on-line reviews in order to make an informed prod- uct choice. This paper introduces OPINE, an unsupervised information- extraction system which mines re- views in order to build a model of im- portant product features, their evalu- ation by reviewers, and their relative quality across products. Compared to previous work, OPINE achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. OPINE’s novel use of relaxation labeling for finding the se- mantic orientation of words in con- text leads to strong performance on the tasks of finding opinion phrases and their polarity.