关键词: 文本情感分类, Fisher判别比, 词性特征, 云向量模型, 核Fisher判别

Abstract: The appearance of massive Web information turns the view extraction from documents into research hotspots. Aiming at the ambiguity in natural language and lower classification precision in text sentiment classification, this paper proposes a novel text sentiment classification algorithm based on Mixed-Fisher feature selection and cloud vector model clustering. In this algorithm, the Fisher's discriminant ratio of different part-of-speech features is computed firstly. The q larger values of Fisher's discriminant ratio features are selected as the candidate features to form Mixed-Fisher feature vector according to the Fisher criterion. These features are combined according to the parts of speech to generate the Mixed-Fisher feature set. And then, cloud vector model is generated based on this Mixed-Fisher feature set for each document. Immediately following, documents are clustered according to their similarity between cloud vector models. Finally, kernel Fisher discriminant (KFD) is adopted as the classifier to judge views. The experimental results demonstrate that the classification precision of the proposed algorithm outperforms traditional vector space model, and the effectiveness of KFD is verified.

Key words: text sentiment classification, Fisher discriminant ratio, part-of-speech feature, cloud vector model, kernel Fisher discriminant