Sentiment classification is the task of labeling a review document according to the polarity of its prevailing opinion (favorable or unfavorable). In approaching this problem, a model builder often has three sources of information available: a small collection 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 generalize 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, primarily through two strategies: incorporating additional derived features into the model and, where possible, using labeled data to estimate their relative influence.
REF extends a sentiment classification model that utilizes unlabeled documents and human-provided information as well as labeled documents.
The Sentimental Factor: Improving Review Classification Via Human-Provided Information
Annual Meeting Of The Association For Computational Linguistics
Computer Science