Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training

Abstract

We investigate the problem of identifying both product properties and opinion words for sentences in a unified process when only a much small labeled corpus is available. Naive Bayesian method is used in this process. Specifically, considering the fact that product properties and opinion words usually co-occur with high frequency in product review articles, a cross- training method is proposed to bootstrap both of them, in which the two sub-tasks are boosted by each other iteratively. Experiment results show that with a much small labeled corpus cross-training could produce both product properties and opinion words which are very close to what Naive Bayesian Classifiers could do with a large labeled corpus..