Research on public opinion analysis of COVID-19 based on machine learning

Jiaxin Jing; Lu Zhang; Duoyao Zhang; Liangbin Yang

doi:10.1117/12.3008681

27 October 2023 Research on public opinion analysis of COVID-19 based on machine learning

Jiaxin Jing, Lu Zhang, Duoyao Zhang, Liangbin Yang

Proceedings Volume 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023); 129222S (2023) https://doi.org/10.1117/12.3008681
Event: The Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 2023, Xiamen, China

Abstract

Social media is a platform for the participation of the whole people, and the "COVID-19" is a topic closely related to the whole people. Based on this, this paper conducts sentiment analysis on the hot topic of social media network public opinion "COVID-19", which can well present the public's emotional evolution process and results of this public opinion event, so as to provide certain guidance for relevant national departments to guide and control public opinion. This article chooses Twitter as the data source platform, collects tweets related to the "COVID-19" in January this year, and builds a simple public opinion analysis system using natural language processing models and different machine learning methods. The research results show that for the topic of "COVID-19", which is a long-lasting topic that is closely related to everyone, the proportion of positive emotions is relatively large, which also protects the security of the country from the level of public opinion. In other words, for a hot topic, when negative emotions prevail, relevant departments need to intervene in time to supervise, guide the direction of public opinion, and maintain social stability.

Citation Download Citation

Jiaxin Jing, Lu Zhang, Duoyao Zhang, and Liangbin Yang "Research on public opinion analysis of COVID-19 based on machine learning", Proc. SPIE 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 129222S (27 October 2023); https://doi.org/10.1117/12.3008681

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