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| OTSRM-Based Approach for Sentiment Evolution and Topic Analysis |
| Wang Kai1, Pan Wei1, Yang Baohua2 |
1.Department of Information Management, Bengbu Medical College, Bengbu 233000
2.School of Information and Computer, Hefei University of Technology, Hefei 230036 |
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Abstract The sentiment evolution of online public topics plays a very important part in the analysis of public opinion, while current methods have problems such as unclear meanings of sentiment topics and inaccurate evaluation of sentiment evolution. This paper introduced sentiment intensity based on the OLDA model and proposed an Online Topic and Sentiment Recognition Mode (OTSRM). By adding sentiment heritability with a β prior parameter, this model established a sentiment evolution channel and obtained two distribution matrices of feature words and sentiment words. Finally, the relative entropy method was proposed to calculate the maximum value of topic sentiment in adjacent time segments, thereby efficiently identifying the topic sentiment of different texts. The effectiveness of OTSRM was validated using five network datasets and compared with other state-of-the-art models. The experiments showed that our approach achieved good results in the recognition of topic sentiment.
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Received: 10 November 2018
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