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Public Opinion Analysis of Online Posts about Vaccine Reactions Based on Topic Modeling and Multi-label Classification
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Chen Donghua,Zhang Runtong
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(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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Abstract
[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.
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Published: 01 March 2022
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