关键词: 文本情感分类, 核Fisher判别, 支持向量机, 向量空间模型, 相关性分析

Abstract: A novel hierarchical text sentiment classification approach based on CCA-VSM classifier and kernel Fisher discriminant is proposed to improve classification accuracy. CCA is utilized to reduce the dimensionality of feature vectors. And then vector space model is built on reduced vector set. By doing this, a novel CCA-VSM classifier is proposed according to the diversity between VSM models. [R] models, which possess smaller diversity, would be selected by CCA-VSM classifier. Kernel Fisher discriminant is used to make judgment. Experiment results show that hierarchical classifier is superior to SVM in text sentiment classification problem, and also show that the method of weight computation and the rule of parts of speech feature selection have big effection on classification results.

Key words: text sentiment classification, kernel Fisher discriminant, Support Vector Machine（SVM）, vector space model, canonical correlation analysis