Using Naïve Bayes Classifier to Distinguish Reviews from Non-review Documents in Chinese
Zhang Zi-qiong, Qiang Ye, Yijun Li · 2007
Reviews are subjective documents expressing opinions or evaluations. In contrast, non-review documents often present factual information objectively. Separating reviews from non-reviews, or subjectivity classification, is potentially important for many text processing applications, such as information extraction and information retrieval. Also, it is a key process in sentiment classification for online customer reviews. As a type of genre classification, the classifications of subjective and objective texts are different from traditional topic-based classifications. Not many studies have been conducted in this domain and most of them were on English texts. Little work has been done on Chinese subjectivity classification. However, the detailed techniques used in English texts can not be applied directly to Chinese due to the different characteristics between these two languages. This paper proposes an approach to perform subjectivity classification on Chinese text based on a supervised machine learning algorithm, Naïve Bayes. Experiment studies have been conducted on two kinds of documents: movie reviews and movie plots written in Chinese. The results show that the performances of the proposed approach are comparable to those of the existing English subjectivity classification studies.