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A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis

Recent statistical and social studies have shown that social media platforms such as Instagram, Facebook, and Twitter contain valuable data that influence human behaviors. This data can be used to track, fight, and control the spread of the COVID-19 and are an excellent asset for analyzing and under...

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Detalles Bibliográficos
Autor principal: Sarirete, Akila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730542/
https://www.ncbi.nlm.nih.gov/pubmed/35013686
http://dx.doi.org/10.1016/j.procs.2021.10.083
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author Sarirete, Akila
author_facet Sarirete, Akila
author_sort Sarirete, Akila
collection PubMed
description Recent statistical and social studies have shown that social media platforms such as Instagram, Facebook, and Twitter contain valuable data that influence human behaviors. This data can be used to track, fight, and control the spread of the COVID-19 and are an excellent asset for analyzing and understanding people’s sentiments. Current levels of willingness to receive a COVID-19 vaccination are still insufficient to achieve immunity standards as stipulated by the World Health Organization (WHO). The present study employs bibliometric analysis to uncover trends and research into sentiment analysis and COVID-19 vaccination. A range of analyses is conducted using the open-source tool VOSviewer and Scopus database from 2020-2021 to acquire a deeper insight and evaluate current research trends on COVID-19 vaccines. The quantitative methodology used generates various bibliometric network visualizations and trends as a function of publication metrics such as citation, geographical attributes, journal publications, and research institutions. Results of network visualization revealed that understanding the the-state-of-the-art in applying sentiment analysis to the COVID-19 pandemic is crucial to local government health agencies and healthcare providers to help in neutralizing the infodemic and improve vaccine acceptance.
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spelling pubmed-87305422022-01-06 A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis Sarirete, Akila Procedia Comput Sci Article Recent statistical and social studies have shown that social media platforms such as Instagram, Facebook, and Twitter contain valuable data that influence human behaviors. This data can be used to track, fight, and control the spread of the COVID-19 and are an excellent asset for analyzing and understanding people’s sentiments. Current levels of willingness to receive a COVID-19 vaccination are still insufficient to achieve immunity standards as stipulated by the World Health Organization (WHO). The present study employs bibliometric analysis to uncover trends and research into sentiment analysis and COVID-19 vaccination. A range of analyses is conducted using the open-source tool VOSviewer and Scopus database from 2020-2021 to acquire a deeper insight and evaluate current research trends on COVID-19 vaccines. The quantitative methodology used generates various bibliometric network visualizations and trends as a function of publication metrics such as citation, geographical attributes, journal publications, and research institutions. Results of network visualization revealed that understanding the the-state-of-the-art in applying sentiment analysis to the COVID-19 pandemic is crucial to local government health agencies and healthcare providers to help in neutralizing the infodemic and improve vaccine acceptance. The Author(s). Published by Elsevier B.V. 2021 2021-12-03 /pmc/articles/PMC8730542/ /pubmed/35013686 http://dx.doi.org/10.1016/j.procs.2021.10.083 Text en © 2021 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Sarirete, Akila
A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title_full A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title_fullStr A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title_full_unstemmed A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title_short A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis
title_sort bibliometric analysis of covid-19 vaccines and sentiment analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730542/
https://www.ncbi.nlm.nih.gov/pubmed/35013686
http://dx.doi.org/10.1016/j.procs.2021.10.083
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