关键词: 文本情感分类, 支持向量机, 情感距离, 转折同化

Abstract: Considering the overall texts orientation identification of Chinese emotional texts and their sentiment express sequence have a great connection, and for the emotional texts, emotion tendency expression which is closer to the end of the texts is more influential to identify whole texts orientation. Therefore, for the short texts which express emotion inconspicuously or complicatedly, this paper considers the sequence of the appearance of the emotional nodes in the text and the sentiment assimilation of the turning point to classify the emotional texts more efficiently. The experimental result on the emotional texts dataset crawled from a shopping site shows that the classification method proposed in this paper is significantly better than the support vector machine classifier that is based on word features simply.

Key words: text sentiment classification, Support Vector Machine（SVM）, sentiment distance, turning assimilation