关键词: 词性嵌入, 特征权重, 情感分类, 粒子群优化

Abstract: The importance of language knowledge is always neglected in traditional feature representation for text sentiment classification. This paper proposes a novel feature weighting approach based on part of speech embedding, in which a feature embedding schema is constructed such that the contribution of noun, verb, adjective and adverb can be embedded into the traditional TF-IDF（Term Frequency-Inverse Document Frequency） weighting, where the best contribution value is obtained by particle swarm optimization algorithm. The support vector machine classifier is used for the Chinese text sentiment classification. In the experiment, the performance of different knowledge is also compared, such as part of speech, sentiment words and their combination. The experimental results show that the proposed method achieves the best classification performance.

Key words: part of speech embedding, feature weighting, sentiment classification, particle swarm optimization