An Improved Term Weighting Scheme for Sentiment Classification

Supervised learning methods are widely used in text sentiment classification. To acquire high classification performance, the effective and precise term weighting scheme plays a prime and necessary role for classification system. The traditional term weighting schemes often ignore the use of the available labeling information as the prior knowledge, which results the expressed relationships between features and class labels are not accurate and adequate enough. Hence, this paper proposes a term weighting scheme which makes use of class contribution of feature terms, and obtains a scheme which takes into account the class contribution, local distribution and global distribution of feature terms. The scheme expresses the relationships between features and class labels as a class contribution degree factor which is based on posterior probability, then combines it with the factors of frequency and inverse document frequency. Compared with a series of term weighting schemes, experimental results on a widely used dataset show that the proposed scheme can significantly improve the performance of sentiment classification.