A method of building Chinese microblog sentiment lexicon was proposed,which adopted the discovery strategies of context entropy for network language, acquired network languages from the secondary filtration by TF-IDF and computed the sentiment weights of network language by SO-PMI algorithm in the labeled corpus. The built lexicon was applied into the analysis experiments of micro-blog sentiment,which was compared with that of naive bayesian classifier. Experiment results showed that the efficacy of classification by the built micro-blog sentimental lexicon was better than that by naive bayesian classifier,and was simple and rapid in the classification process.
