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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 37-47    DOI: 10.11925/infotech.2096-3467.2017.1107
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Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity
Xiufang Wang,Shu Sheng(),Yan Lu
College of Computer of Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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[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.

Key wordsPublic Opinion Analysis      Sentiment Analysis      Topic Clustering      Sentiment Intensity Analysis     
Received: 07 November 2017      Published: 11 July 2018

Cite this article:

Xiufang Wang,Shu Sheng,Yan Lu. Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity. Data Analysis and Knowledge Discovery, 2018, 2(6): 37-47.

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