Attitudes Evaluation Toward COVID-19 Pandemic: An Application of Twitter Sentiment Analysis and Latent Dirichlet Allocation

Abstract

USA is among the countries that have been considerably affected by the COVID-19 to contain the largest proportion of cases globally. This research aims at investigating the American Community opinions polarity toward the outbreak of the virus in the US throughout twitter social media platform. Further, a topic modeling approach was employed to gain insights about the most discussed topic by the US community during pandemic. A total number of 1,385,469 tweets were collected for the purpose of the study over the period of early February to late April. In addition, unsupervised approaches were employed in the analysis including VADER lexicon for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modelling. The main findings of the research showed that the largest share of the collected tweets is of positive sentiment followed by negative and neutral. Further, temporal sentiment analysis on monthly basis in comparison with COVID-19 cases showed how the tweets polarity changed over time from early February to late April. In total, the polarity of the tweets was negative before the virus outbreak and positive during the outbreak. In addition, LDA analysis showed that the overall discussed topics tend are oriented toward economy, politics, and the spread of the virus outside the US in February while March and April topics are oriented toward discussing the prevention from the virus as well as spread of the virus inside USA.