Identifying Potential Trending Topics of Online Public Opinion
Ding Shengchun1,2(),Yu Fengyang1,Li Zhen1
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2Jiangsu Social Public Security Science and Technology Collaborative Innovation Center, Nanjing 210094, China
[Objective] This paper tries to find potential trending topics from the online data, aiming to help government or enterprises monitor and guide public opinion.[Methods] First, we collected topics of public opinion with microblog’s real-time data stream. Then, we identified features of trending topics. Finally, we compared the performance of the Logistic Regression and SVM models for predicting potential trending topics.[Results] The Logistic Regression model is more capable of finding potential trending topics (recall=0.89) than SVM.[Limitations] More research is needed to examine our model with other social media platforms.[Conclusions] The proposed model could effectively identify potential trending topics of online public opinion.
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