How Do People View COVID-19 Vaccines


The COVID-19 pandemic has been the most devastating public health crisis in the recent decade and vaccination is anticipated as the means to terminate the pandemic. People's views and feelings over COVID-19 vaccines determine the success of vaccination. This study was set to investigate sentiments and common topics about COVID-19 vaccines by machine learning sentiment and topic analyses with natural language processing on massive tweets data. Findings revealed that concern on COVID-19 vaccine grew alongside the introduction and start of vaccination programs. Overall positive sentiments and emotions were greater than negative ones. Common topics include vaccine development for progression, effectiveness, safety, availability, sharing of vaccines received, and updates on pandemics and government policies. Outcomes suggested the current atmosphere and its focus over the COVID-19 vaccine issue for the public health sector and policymakers for better decision-making. Evaluations on analytical methods were performed additionally.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: Copyright 2022. This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.
Uncontrolled Keywords: Information Systems and Management,Management Science and Operations Research,Strategy and Management,Computer Science Applications,Business and International Management
Publication ISSN: 1533-7995
Last Modified: 03 Jul 2024 17:05
Date Deposited: 19 Sep 2022 08:21
Full Text Link:
Related URLs: https://www.igi ... /article/300817 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-10
Published Online Date: 2022-08-16
Accepted Date: 2022-06-01
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Ng, Chun Yu
Xu, Qianwen Ariel
Zayed, Mohamed Bin
Hossain, M. A.



Version: Published Version

License: Creative Commons Attribution

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