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

The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture important expressions like "could have been better", 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 training set and thus fail to yield robust predictors. This paper overcomes the limitations of these two models, by introducing a novel kind of bag-of-opinions representation, where an opinion, within a review, consists of three components: a root word, a set of modifier words from the same sentence, and one or more negation words. Each opinion is assigned a numeric score which is learned, by ridge regression, from a large, domain-independent corpus of reviews. For the actual test case of a domain-dependent review, the review's ra...

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Lizhen Qu, Georgiana Ifrim, Gerhard Weikum
• Keywords: COLING 2010, Computational Linguistics, Review, Review Rating Prediction, Ridge Regression