Bootstrapping Both Product Features and Opinion Words from Chinese Customer Reviews with Cross-Inducing

We consider the problem of 1 identifying product features and opinion words in a unified process from Chinese customer reviews when only a much small seed set of opinion words is available. In particular, we consider a problem setting motivated by the task of identifying product features with opinion words and learning opinion words through features alternately and iteratively. In customer reviews, opinion words usually have a close relationship with product features, and the association between them is measured by a revised formula of mutual information in this paper. A bootstrapping iterative learning strategy is proposed to alternately both of them. A linguistic rule is adopted to identify lowfrequent features and opinion words. Furthermore, a mapping function from opinion words to features is proposed to identify implicit features in sentence. Empirical results on three kinds of product reviews indicate the effectiveness of our method.