Research on Support Vector Machine Early Warning Model of Internet Public Opinion Based on Gray Correlation Analysis

Qing Yu (), Huiying Du () and Genxiang Gao ()
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Qing Yu: Beijing Information Science & Technology University
Huiying Du: Beijing Information Science & Technology University
Genxiang Gao: Beijing Information Science & Technology University

A chapter in LISS 2024, 2025, pp 636-648 from Springer

Abstract: Abstract With the widespread use of the Internet and social media, the need for effective monitoring of the state of public opinion dissemination has become increasingly urgent. The establishment of a comprehensive early warning mechanism for online public opinion has become an important foundation for providing specific countermeasures for enterprises and governmental departments. In this paper, we take the quantitative indicators of public opinion publishers, public opinion subjects, and dissemination dispersion as the starting point to construct a network public opinion early warning indicator system. Gray correlation analysis and K-means are used to grade public opinion events, and the grading results are used as the standard for training the public opinion early warning model. In the process of model construction, the entropy weight method is used to calculate the weights of each index, and then the principal component analysis and the support vector machine model are combined to determine the online public opinion early warning level. The experimental results show that the proposed model can scientifically classify the severity level of network public opinion emergencies and accurately predict the warning level of emergencies by combining the multidimensional information of public opinion events. This provides a new early warning method and idea for the network public opinion monitoring system.

Keywords: internet public opinion; indicator system; gray correlation analysis; support vector machine (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_49

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DOI: 10.1007/978-981-96-9697-0_49

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