Paper: The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns

ACL ID C10-1103
Title The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

The problem addressed in this paper is to predict a user’s numeric rating in a prod- uct review from the text of the review. Un- igram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture impor- tant expressions like “could have been bet- ter”, which are essential for prediction models of ratings. N-grams of words, on the other hand, capture such phrases, but typically occur too sparsely in the train- ing set and thus fail to yield robust pre- dictors. This paper overcomes the limita- tions of these two models, by introducing a novel kind of bag-of-opinions represen- tation, where an opinion, within a review, consists of three components: a root word, a set of modifier words from the same sen- tence, and one or more negation words....