Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media

The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Mining and analyzing social media text information can n...

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Bibliographic Details
Published in:Journal of medical Internet research 2024-01, Vol.26 (6), p.e47508-e47508
Main Authors: Guo, Feipeng, Liu, Zixiang, Lu, Qibei, Ji, Shaobo, Zhang, Chen
Format: Article
Language:English
Subjects:
Algorithms
Application programming interface
Bidirectionality
Blogs
China
Classification
Communications software
COVID-19
Data mining
Dissemination
Emotions
Epidemics
Humans
Infectious diseases
Influenza
Information dissemination
Iran
Machine learning
Original Paper
Outdoor air quality
Pandemics
Public health
Public Opinion
Regression analysis
Sentiment Analysis
Social Media
Social networks
Spatial analysis
Text categorization
User behavior
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Summary:The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/47508