Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic

Abstract

In this paper, we analyze over 18 million coronavirus related Twitter messages collected between March 1, 2020 and May 31, 2020. We perform sentiment analysis using VADER, a rule-based supervised machine learning model, to evaluate the relationship between public sentiment and number of COVID-19 cases. We also look at the frequency of mentions of a country in tweets and the rise in its' daily number of COVID-19 cases. Some of our findings include the discovery of a correlation between the number of tweets mentioning Italy, USA, and UK and the daily increase in new COVID-19 cases in these countries.