Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse
COVID Twitter screenshot

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.

Team Members
  • Abraham Sanders
  • Rachael White
  • Lauren Severson
  • Rufeng Ma
  • Richard McQueen
  • Haniel C. Alcantara Paulo
  • Yucheng Zhang
  • John S. Erickson
  • Kristin P. Bennett
Parent Projects