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On Public Sentiment and Topic Mining during the COVID-19 Pandemic Based on Sina Weibo Comment Data
Volume 18, Issue 5 (2020): Special Issue S1 in Chinese (with abstract in English), pp. 875–888
Xiaomeng Du   Wei Huang   Yijing Liu     All authors (4)

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https://doi.org/10.6339/JDS.202012_18(5).0003
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

In the wake of the COVID-19 outbreak, the public resorted to Sina Weibo as a major platform for the trend of the pandemic. Research on public sentiment and topic mining of major public sentiment events based on Sina Weibo’s comment data is important for understanding the trend of public opinions during major epidemic outbreaks. Based on classification of the Chinese language into emotion categories in psychology, we use open source tools to build naive Bayesian models to classify Weibo comments. Visualization of comment topics is achieved with word co-occurrence network methods. Commented topics are mined with the help of the latent Dirichlet distribution model. The results show that the psychological sentiment classification combined with the naive Bayesian model can reflect the evolvement of public sentiment during the epidemic, and that the latent Dirichlet distribution model and word co-occurrence network can effectively mine the topics of public concerns.

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Keywords
Dirichlet distribution emotional classification naive Bayes visualization word co-occurrence

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