Improving Chinese Sentence Polarity Classification via Opinion Paraphrasing

While substantial studies have been achieved on sentiment polarity classification to date, lacking enough opinion-annotated corpora for reliable t rain ing is still a challenge. In this paper we propose to improve a supported vector machines based polarity classifier by enriching both training data and test data via opinion paraphrasing. In particular, we first extract an equivalent set of attributeevaluation pairs from the training data and then exploit it to generate opinion paraphrases in order to expand the training corpus or enrich opinionated sentences for polarity classification. We tested our system over two sets of online product reviews in car and mobilephone domains. The experimental results show that using opinion paraphrases results in significant performance improvement in polarity classification.