Paper: Towards Answering Opinion Questions: Separating Facts From Opinions And Identifying The Polarity Of Opinion Sentences

ACL ID W03-1017
Title Towards Answering Opinion Questions: Separating Facts From Opinions And Identifying The Polarity Of Opinion Sentences
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion ques- tion answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating be- tween documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opin- ions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective be- ing expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicatin...