Paper: The Sentimental Factor: Improving Review Classification Via Human-Provided Information

ACL ID P04-1034
Title The Sentimental Factor: Improving Review Classification Via Human-Provided Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

Sentiment classification is the task of labeling a re- view document according to the polarity of its pre- vailing opinion (favorable or unfavorable). In ap- proaching this problem, a model builder often has three sources of information available: a small col- lection of labeled documents, a large collection of unlabeled documents, and human understanding of language. Ideally, a learning method will utilize all three sources. To accomplish this goal, we general- ize an existing procedure that uses the latter two. We extend this procedure by re-interpreting it as a Naive Bayes model for document sentiment. Viewed as such, it can also be seen to extract a pair of derived features that are linearly combined to predict sentiment. This perspective allows us to improve upon previous methods, pri...