Machine Learning-Based Sentiment Analysis of Twitter COVID-19 Vaccination Responses

The COVID-19 pandemic has caused significant fear, anxiety, and complex emotions or feelings in a large number of people. A global vaccination campaign to end the SARS-CoV-2 epidemic is now in progress. People’s feelings have become more complex and varied since the introduction of vaccinations against coronavirus. The use of social media platforms such as Twitter enables users to communicate with one another and share information and perspectives on a wide variety of topics, spanning from local to international concerns, from global to personal. Twitter will prove to be a helpful source of information that can be tracked regarding views and sentiments regarding the SARS-CoV-2 vaccination. To better understand public views, concerns, and emotions that may influence the achievement of herd immunity targets and limit the pandemic’s impact, this study uses deep learning to identify the themes and sentiments in the public about COVID-19 immunization on Twitter. Moreover, this paper consists of a detailed explanation of the sentiment analysis with their challenges, classification, approaches, applications, and VADER.