Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse
Sanders, A.; White, R.; Severson, L.; Ma, R.; McQueen, R.; Alcanatara Paulo, H. C.; Zhang, Y.; Erickson, J. S.; Bennett, K. P.
Show 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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