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Public Opinion Analysis of Online Posts about Vaccine Reactions Based on Topic Modeling and Multi-label Classification
Chen Donghua,Zhang Runtong
(School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China) (School of Economics and Management, Beijing Jiaotong University, 100044, China)
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[Objective] Topic mining and multi-label classification are leveraged to provide decision support for vaccine adverse event (VAE) monitoring and public opinion analysis.

[Methods] We propose a latent Dirichlet allocation-based VAE topic modeling method with the use of domain knowledge and accordingly develop a public opinion analysis method for vaccine-associated posts based on different strategies of multi-label classification. Finally, we discuss the relationships between user vaccine-related sentiments and the patterns of online user behaviors.

[Results] The use of sentiment dictionaries and MedDRA terminology sets improves the accuracy of VAE-related sentiment analysis by up to 15.17%. The One-vs-Rest-based methods achieve an accuracy of up to 97.15% while the other methods merely achieve an average accuracy of 80%.

[Limitations] Lots of non-standard terms about VAE-associated posts on social media have great influence in vaccine-related information extraction. Further use of controlled medical terminology and multimodal information analysis will improve the accuracy of vaccine-related public opinion analysis.

[Conclusions] VAE topic mining and sentiment analysis improve the accuracy of public opinion analysis and decision support for people after massive vaccination.

Key words Public Opinion Analysis      Vaccine Adverse Event      Topic Mining      Sentiment Analysis      Multi-label Classification      
Published: 01 March 2022
ZTFLH:  TP391.1  

Cite this article:

Chen Donghua, Zhang Runtong. Public Opinion Analysis of Online Posts about Vaccine Reactions Based on Topic Modeling and Multi-label Classification . Data Analysis and Knowledge Discovery, 0, (): 1-.

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