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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 |
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Abstract [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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Received: 24 June 2019
Published: 26 April 2020
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Corresponding Authors:
Ding Shengchun
E-mail: todingding@163.com
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