关键词: 情感词典, 情感分类, PMI算法, 朴素贝叶斯

Abstract: Massive Internet text sentiment analysis is currently a hot research topic. This paper describes a method on Chinese text sentiment lexicon construction. This method improves the pointwise mutual information (PMI) algorithm for computing the weights of general sentiment lexicon, by selecting several sentiment seed words and drawing upon the total result numbers from search engine. In order to examine the validity of this method, this paper uses the established sentiment lexicon for text sentiment, and compares the classification effects of the method based on sentiment lexicon with those of na?ve Bayesian classifier. The experimental results indicate that the high-quality sentiment lexicon can effectively choose and classify the sentiment characteristics, and has a stable classification function.

Key words: sentiment lexicon, sentiment classification, pointwise mutual information (PMI), naïve Bayes