Systematic study of twitter sentiment analysis on COVID-19 datasets using various techniques

Abstract

In this research we have done COVID-19 sentiment analysis using twitter datasets. This research paper gives an empirical analysis of public tweets and speeches related to the COVID-19 pandemic. Using natural language processing techniques, we analysed a number of tweets, news articles, and other online content to understand attitudes and sentiments related to the pandemic. Our research shows that there are many different opinions about COVID-19, including fear, uncertainty, anger and hope. We also identified key themes driving this sentiment, including government policies, vaccine development and social distancing measures. Findings from this study can inform healthcare communication strategies and help policymakers better understand public sentiment in times of crisis.