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Analytics of Public Reactions to the COVID-19 Vaccine on Twitter Using Sentiment Analysis and Topic Modelling
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Author(s): Mazin Salih Kadhim Abadah (School of Computer Sciences, Universiti Sains Malaysia, Malaysia), Pantea Keikhosrokiani (School of Computer Sciences, Universiti Sains Malaysia, Malaysia)and Xian Zhao (School of Computer Sciences, Universiti Sains Malaysia, Malaysia)
Copyright: 2023
Pages: 33
Source title:
Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes
Source Author(s)/Editor(s): David Valle-Cruz (Universidad Autónoma del Estado de México, Mexico), Nely Plata-Cesar (Universidad Autónoma del Estado de México, Mexico)and Jacobo Leonardo González-Ruíz (Universidad Autónoma del Estado de México, Mexico)
DOI: 10.4018/978-1-6684-5624-8.ch008
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Abstract
The number of new COVID-19 infections and deaths is still increasing worldwide, which led governments to take a series of mandatory actions. The COVID-19 vaccine announcement kindled the various rays of emotions among the social media users. Thus, this chapter aims to discover public reaction regarding the COVID-19 vaccine posts on a social media platform, specifically Twitter, to extract the most discussed topics during the period April 25, 2021 to May 2, 2021. The extraction was based on a dataset of English tweets pertinent to the COVID-19 vaccine. The Latent Dirichlet Allocation (LDA) was adopted for topics extraction whereas VADER lexicon-based approach was applied for sentiment analysis. Based on the results, most tweets expressed neutral and positive opinions about the COVID-19 vaccine. Regarding the latent themes discovered about the vaccine, most of topics have exposed the public trust towards the COVID-19 vaccine compared with the mistrust ones. This study can assist governments and policy makers to track public opinions for better decision-making during pandemics.
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