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Research on clustering analysis of Internet public opinion

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Abstract

In order to enhance the real-time performance of Internet public opinion recognizing and early warning, and improve the accuracy of the analysis of Internet public opinion for hot spots, similarity analysis methods of Internet public opinion are put forward. Firstly, web crawler technology is introduced for obtaining accurate and comprehensive public opinion. Secondly, propose similarity algorithms from the aspects of known and unknown of the subject. At the same time, considering the uncertainty and fuzzy of Internet public opinion, the concept of information entropy is introduced, and present a similarity analysis approach of Internet public opinion based on information entropy, and can cluster and identify hot spots and crisis events of Internet public opinion. Experimental results show that the proposed methods can quickly obtain the Internet public opinion, and has high accuracy rate of clustering, which provide an important technical support for Internet public opinion monitoring and recognizing.

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Acknowledgements

The authors would like to thank for financial support by youth fund project of the humanities and social sciences of Education Ministry (No. 15YJC870004), science and technology innovation team of XiangNan University (Recognition and analysis based on big data public opinion), Social Sciences fund project of Hunan Province (No. 13YBA302) and education Science in Hunan province in 12th Five-Year planning project (XJK014CGD081, XJK011BXJ004).

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Authors and Affiliations

  1. College of Software and Communication Engineering, XiangNan University, Chenzhou, 423000, People’s Republic of China

    XueGang Chen, Sheng Duan & Lu-da Wang

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  1. XueGang Chen
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  2. Sheng Duan
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  3. Lu-da Wang
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Correspondence to XueGang Chen.

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Chen, X., Duan, S. & Wang, Ld. Research on clustering analysis of Internet public opinion. Cluster Comput 22 (Suppl 3), 5997–6007 (2019). https://doi.org/10.1007/s10586-018-1781-3

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  • DOI: https://doi.org/10.1007/s10586-018-1781-3

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