Analysis and Mining of Internet Public Opinion Based on LDA Subject Classification

This paper uses Python, R language, Gephi and other software to crawl and classify the comment content of Weibo hot search events. Using word cloud, co-occurrence social network graphs, LDA topic classification visualization methods, this paper regularizes and integrates public opinions of hot event...

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Bibliographic Details
Published inJournal of web engineering Vol. 20; no. 8; p. 2457
Main Authors Zhang, Mei, Su, Huihui, Wen, Jinghua
Format Journal Article
LanguageEnglish
Published Milan River Publishers 01.01.2021
Subjects
Big Data
Classification
COVID-19
Data collection
Emergencies
Graphical representations
Information dissemination
Information technology
Internet
Public health
Public opinion
Public sector
Python
Social networks
Trends
China
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Summary:This paper uses Python, R language, Gephi and other software to crawl and classify the comment content of Weibo hot search events. Using word cloud, co-occurrence social network graphs, LDA topic classification visualization methods, this paper regularizes and integrates public opinions of hot events. Through this research, we can get the influence of public opinion mediators, public opinion objects, and government forces on the network public opinion and put forward corresponding improvement suggestions. We hope to contribute to the government’s governance and prevention of online public opinion during the spread of COVID-19 and other public hot events.
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ISSN:1540-9589
1544-5976
DOI:10.13052/jwe1540-9589.20811