Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques

Abstract

The quest to create a vaccine for covid-19 has rekindled hope for most people worldwide, with the anticipation that a vaccine breakthrough would be one step closer to the end of the deadly Covid-19. The pandemic has had a bearing on the use of Twitter as a communication medium to reach a wider audience. This study examines Covid-19 vaccine-related discussions, concerns, and Twitter emerged sentiments about Covid-19 vaccine rollout program. Natural Language Processing (NLP) techniques were applied to analyze Covid-19 vaccine-related tweets. Our analysis identified popular n-grams and salient themes such as "vaccine health information," "vaccine distribution and administration," "vaccine doses required for immunity," and "vaccine availability." We apply machine learning algorithms and evaluate their performance using the standard metrics, namely accuracy, precision, recall, and f1-score. Support Vector Machine (SVM) classifier proves to be the best fit on the dataset with 84.32% accuracy. The research demonstrates how Twitter data and machine learning methods can study the evolving public discussions and sentiments concerning the Covid-19 vaccine rollout program

Author Biography

Samuel Kofi Akpatsa, University of Electronic Science and Technology of China

Research Assistant, School of Information and Software Engineering

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Authors

  • Samuel Kofi Akpatsa University of Electronic Science and Technology of China
  • Xiaoyu Li University of Electronic Science and Technology of China
  • Hang Lei University of Electronic Science and Technology of China
  • Victor-Hillary Kofi Setornyo Obeng University of Electronic Science and Technology of China

DOI:

https://doi.org/10.31449/inf.v46i1.3483

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Published

03/15/2022

How to Cite

Akpatsa, S. K., Li, X., Lei, H., & Obeng, V.-H. K. S. (2022). Evaluating Public Sentiment of Covid-19 Vaccine Tweets Using Machine Learning Techniques. Informatica, 46(1). https://doi.org/10.31449/inf.v46i1.3483

Issue

Vol. 46 No. 1 (2022)

Section

Regular papers

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