Sentiment classification for Chinese reviews: a comparison between SVM and semantic approaches

Abstract

Web content mining is intended to help people to discover valuable information from large amount of unstructured data on the Web. Sentiment classification aims to mining the Web content of product reviews by classifying the reviews into positive or negative opinions. Such kind of classification approaches could help both consumers and sellers in making their decisions. But it is also a complicated task with great challenge. This paper conducted a comparison between the SVM approach and semantic approach for sentiment classification of Chinese reviews and also proposed some improvement for sentiment classification approaches. Experimental result indicated that, compared with previous researches for English reviews, the performance of both approaches for Chinese reviews sentiment classification are acceptable, while the support vector machine approach has better performance than the semantic orientation approach.