Abstract：Micro-blog sentiment analysis is a key technique of public opinion research for social networks. Micro-blog emoticons and sentiment words are both of intuitive called as explicit emotion features, while the content semantics are called implicit features which sometimes are very important for micro-blog emotion discrimination. Therefore, in this paper, a new systematic methodology for sentiment analysis is proposed using both explicit and implicit emotion features. At first, the sentiment analysis dictionary, the glossary of social networking terms, as well as the emoticon library, are all initialized. Then, the text micro-blog frequent word sets are defined. According to the feature set of words, the initial micro-blog clusters can be directly generated depending on the maximum frequent item sets. Furthermore, as to solve the micro-blog overlap problem between multiple initial clusters, an efficient elimination method is proposed employing the extended membership degree of the short-message semantic. Finally, the semantic similarity matrix for each separated cluster is defined, based on which a hierarchical sentiment clustering for micro-blogs is conducted. Taking the well-known contest NLPCC2013 in China as instance, the efficiency of our proposed method is proved by the comparing experiments. At last, a real-world case is also done to exactly show the emotion change from Chinese micro-blogs for the Malaysia Airlines Disappear Incident during March 8 to Spril 8, 2014
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2016, Vol. 30 
