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| Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity |
Wang Xiufang, Sheng Shu( ), Lu Yan |
| College of Computer of Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China |
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Abstract [Objective] This paper builds a model to monitor the trending topics from microblogs, aiming to deal with the issues of text drifting and quantitation of sentimental polarity. [Methods] First, we proposed a public opinion analysis model based on topic clustering and sentiment intensity. Then, we used the time series regression analysis to predict the sentimental changes among the trending topics. [Results] The prediction accuracy of our model reached 88.97%, which was about 7% higher than the iLab-Edinburgh model. [Limitations] More research is needed to study the early warning mechanisms for emergency events. [Conclusions] The proposed model could improve the prediction accuracy of sentimental changes, which provides an effective way to analyze the public opinion from microblogs.
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Received: 07 November 2017
Published: 11 July 2018
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