A Document Sentiment Classification System Based on the Feature Weighting Method Improved by Measuring Sentence Sentiment Intensity
Jae-Won Hwang, Youngjoong Ko · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2009
This paper proposes a new feature weighting method for document sentiment classification. The proposed method considers the difference of sentiment intensities among sentences in a document. Sentiment features consist of sentiment vocabulary words and the sentiment intensity scores of them are estimated by the chi-square statistics. Sentiment intensity of each sentence can be measured by using the obtained chi-square statistics value of each sentiment feature. The calculated intensity values of each sentence are finally applied to the TF-IDF weighting method for whole features in the document. In this paper, we evaluate the proposed method using support vector machine. Our experimental results show that the proposed method performs about 2.0% better than the baseline which doesn't consider the sentiment intensity of a sentence.