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• W4300964701 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." @default.
• W4300964701 created "2022-10-04" @default.
• W4300964701 creator A5060184929 @default.
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• W4300964701 date "2022-09-16" @default.
• W4300964701 modified "2023-09-29" @default.
• W4300964701 title "Analytics of Public Reactions to the COVID-19 Vaccine on Twitter Using Sentiment Analysis and Topic Modelling" @default.
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• W4300964701 doi "https://doi.org/10.4018/978-1-6684-5624-8.ch008" @default.
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