Abstract
Currently, most sentiment analysis of microblog has been focused on coarse-grained sentiment analysis, but fine-grained sentiment is better for reflecting the opinion of the public when they are facing the social focus. Therefore, a hybrid strategy which is a combination of Naive Bayesian and two-layer CRFs is put forward, which has been applied to the finegrained sentiment analysis of Chinese microblog. First, microblog is classified into two types: sentiment and non-sentiment by using Naive Bayesian classification algorithm. And then the first-layer CRFs model is built for the topic emotional sentence. Finally CRFs algorithm is used again to do multi-classification to assign a specific sentiment category. Experimental results show that a good result in sentiment identification based on the combination of Naive Bayesian and CRFs, and also show the advantage of the combination of Naive Bayesian and CRFs interrelated with emotional sentence extraction based on CRFs.
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