Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15–17 March 2024, Changsha, China

Research Article

Research on Theme Identification of Public Opinion in Colleges and Universities

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  • @INPROCEEDINGS{10.4108/eai.15-3-2024.2346412,
        author={Xueyan  Liu and Xuefeng  Long and Hejie  Chen},
        title={Research on Theme Identification of Public Opinion in Colleges and Universities },
        proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China},
        publisher={EAI},
        proceedings_a={PMIS},
        year={2024},
        month={6},
        keywords={public opinion in higher education lda thematic modeling theme co-occurrence},
        doi={10.4108/eai.15-3-2024.2346412}
    }
    
  • Xueyan Liu
    Xuefeng Long
    Hejie Chen
    Year: 2024
    Research on Theme Identification of Public Opinion in Colleges and Universities
    PMIS
    EAI
    DOI: 10.4108/eai.15-3-2024.2346412
Xueyan Liu1, Xuefeng Long1,*, Hejie Chen2
  • 1: Chongqing University of Posts and Telecommunications
  • 2: Beijing Institute of Graphic Communication
*Contact email: 745640353@qq.com

Abstract

The 51st Statistical Report on Internet Development in China shows that the number of Internet users in China is 1.067 billion, and the Internet penetration rate reaches 75.6%, among which the percentage of Chinese Internet users among teenagers will reach 26.6%, and college students are an important group of teenage Internet users, whose knowledge, feelings, intentions and behaviors are all influenced by the Internet. Internet public opinion in colleges and universities is the focus and source of social public opinion, and is more likely to trigger discussions among netizens to form hotspots of public opinion. In this paper, based on the text data of Baidu posting in Chongqing universities, we extracted the topic words of public opinion through the LDA topic model, and constructed the topic co-occurrence network in each stage by using the Gephi social network analysis tool through the topic word co-occurrence method. The results of the study show the themes of university online public opinion at different stages and the heat changes of different themes at different time stages.