A public opinion classification algorithm based on micro-blog text sentiment intensity: Design and implementation

on the features of short content and nearly real- time broadcasting velocity of micro-blog information, our lab constructed a public opinion corpus named MPO Corpus. Then, based on the analysis of the status of the network public opinion, it proposes an approach to calculate the sentiment intensity from three levels on words, sentences and documents respectively in this paper. Furthermore, on the basis of the MPO Corpus and HowNet Knowledge-base and sentiment analysis set, the feature words' semantic information is brought into the traditional vector space model to represent micro-blog documents. At the same time, the documents are classified by the subjects and sentiment intensity. Therefore, the experiment result indicates that the proposed method improves the efficiency and accuracy of the micro-blog content classification, the public opinion characteristics analysis and supervision in this paper. Thus, it provides a better technical support for content auditing and public opinion monitoring for micro-blog platform.

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