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At the beginning of the COVID-19 outbreak in March, we observed one of thelargest stock market crashes in history. Within the months following this, avolatile bullish climb back to pre-pandemic performances and higher. In thispaper, we study the stock market behavior during the initial few months of theCOVID-19 pandemic in relation to COVID-19 sentiment. Using text sentimentanalysis of Twitter data, we look at tweets that contain key words in relationto the COVID-19 pandemic and the sentiment of the tweet to understand whethersentiment can be used as an indicator for stock market performance. There hasbeen previous research done on applying natural language processing and textsentiment analysis to understand the stock market performance, given howprevalent the impact of COVID-19 is to the economy, we want to further theapplication of these techniques to understand the relationship that COVID-19has with stock market performance. Our findings show that there is a strongrelationship to COVID-19 sentiment derived from tweets that could be used topredict stock market performance in the future.

Mining the Relationship Between COVID-19 Sentiment and Market Performance