An Analysis of Public Sentiment around Face Mask During Covid-19

The impact of the covid-19 pandemic continues to be felt as we face the aftermath the pandemic has caused globally in terms of loss of life, economic damage, civil unrest, and political instability. Mixed messaging and partisanship from leaders worldwide have made proposed solutions a divisive political stance. The polarization of covid-19 and the sentiments around proposed solutions warrant investigation; thus, this study's objective is to examine the sentiments and topics in global public discourse around one of the proposed measures to contain the spread of covid-19; mask usage. This study looks to examine the sentiments around the global discourse of Twitter users on mask usage to investigate the relationship between public sentiment, fluctuations in covid-19 cases, and topics of discussion and influential events through sentiment analysis and topic modeling. Sentiment analysis and Topic Modeling are conducted on the dataset after it is preprocessed to analyze the abstract topics discussed over the time period. The results are then visualized to highlight key topics present in the discussion around mask usage and the accompanying sentiment that is then examined in relation to global events and case numbers during the same time. The goal is to understand how the nature of global discourse can impact the perceptions of mask usage and how that relates to behavior that continues the spread of the pandemic.