Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.
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ABSTRACT:
SUBMITTER: Xue J
PROVIDER: S-EPMC7518625 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
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Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.
Xue Jia J Chen Junxiang J Chen Chen C Zheng Chengda C Li Sijia S Zhu Tingshao T
PloS one 20200925 9
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outb ...[more]
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