Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse
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Authors
Sanders, Abraham
White, R. C.
Severson, L. S.
Ma, R.
McQueen, R.
Alcântara Paulo, H. C.
Zhang, Y.
Erickson, John S.
Bennett, Kristin P.
Issue Date
2021-05
Type
Article
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Alternative Title
Abstract
In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19. We find that topic clustering based on mask-related Twitter data offers revealing insights into societal perceptions of COVID- 19 and techniques for its prevention. We observe that the volume and polarity of mask-related tweets has greatly increased. Importantly, the analysis pipeline presented may be leveraged by the health community for qualitative assessment of public response to health intervention techniques in real time.
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Full Citation
Sanders, A. C., White, R. C., Severson, L. S., Ma, R., McQueen, R., Alcântara Paulo, H. C., Zhang, Y., Erickson, J. S., & Bennett, K. P. Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2021, 555–564. https://pubmed.ncbi.nlm.nih.gov/34457171/. May 2021
Publisher
AMIA