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


  • Mukul Sanodiya Student, Department of Computer Science Engineering, Lakshmi Narain College of Technology (LNCT), Bhopal, Madhya Pradesh, India
  • Sadhna K. Mishra Associate Professor, Department of Computer Science Engineering Lakshmi Narain College of Technology (LNCT), Bhopal, Madhya Pradesh, India


COVID-19, Vaccination, Sentiment Analysis, Opinion Mining, social media, Twitter (now X), Valence Aware Dictionary and Sentiment Reasoner (VADER), machine learning


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 the coronavirus. The use of social media platforms such as X (formally known 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. This study employs deep learning techniques to discern prevalent themes and sentiments within public discourse on Twitter concerning COVID-19 immunization. The goal is to gain a comprehensive understanding of public views, concerns, and emotions that could impact the attainment of herd immunity targets and mitigate the pandemic's effects. Moreover, this paper consists of a detailed explanation of the sentiment analysis with their challenges, classification, approaches, applications, and VADER (Valence Aware Dictionary and Sentiment Reasoner).


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How to Cite

M. . Sanodiya and S. K. . Mishra, “Deep Learning-Based Sentiment Analysis of Twitter COVID-19 Vaccination Responses”, JoSETTT, vol. 10, no. 3, pp. 1–14, Jan. 2024.