Analyzing Public Opinion on Three-Child-Policy with Sentiment Classification and Keyword Extraction
Meng Fansi1,Zhong Han1(),Shi Shuicai2,Xie Zekun1
1School of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China 2TRS Information Technology Co., Ltd., Beijing 100101, China
[Objective] This paper studies the public opinion on the three-child-policy in different Chinese provinces. [Context] Existing research on this issue addresses public opinion from the Web as a whole, and ignores the demands or concerns from individual province. These studies’ research methods are rather simple with single data source. [Methods] Firstly, we analyzed the public opinion on three-child-policy with time series method from the statistical perspective. Then, we examined their sentiments with the SVM model, and extracted keywords from the negative opinion with the CRF model. Third, we created word clouds for these keywords. Finally, we conducted research on these public opinion in different provinces and generated word clouds for them. We also examined the ties between political or economic statistics and the negative key words from different provinces. [Results] The three-child-policy was more popular than other policies during the same period. The public opinion was dominated by neutral sentiments (60.56%), followed by the positive (35.15%) and the negative ones (4.29%). Public concerns in different provinces were different and correlated to the political, economic and ecological factors. [Conclusions] Different provinces should adopt customized public opinion guidance to support the three-child-policy, which will address people’s concerns more effectively.
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Meng Fansi,Zhong Han,Shi Shuicai,Xie Zekun. Analyzing Public Opinion on Three-Child-Policy with Sentiment Classification and Keyword Extraction. Data Analysis and Knowledge Discovery, 2022, 6(10): 142-150.
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