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Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage

This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the...

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Autores principales: SV, Praveen, Lorenz, Jose Manuel, Ittamalla, Rajesh, Dhama, Kuldeep, Chakraborty, Chiranjib, Kumar, Daruri Venkata Srinivas, Mohan, Thivyaa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692646/
https://www.ncbi.nlm.nih.gov/pubmed/36423024
http://dx.doi.org/10.3390/vaccines10111929
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author SV, Praveen
Lorenz, Jose Manuel
Ittamalla, Rajesh
Dhama, Kuldeep
Chakraborty, Chiranjib
Kumar, Daruri Venkata Srinivas
Mohan, Thivyaa
author_facet SV, Praveen
Lorenz, Jose Manuel
Ittamalla, Rajesh
Dhama, Kuldeep
Chakraborty, Chiranjib
Kumar, Daruri Venkata Srinivas
Mohan, Thivyaa
author_sort SV, Praveen
collection PubMed
description This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the process of COVID-19 booster dose vaccinations. Understanding the emotions and concerns of the citizens regarding the health policy being implemented will assist the government, health policy officials, and policymakers implement the policy efficiently so that desired results can be achieved. Seventy-six thousand nine hundred seventy-nine tweets were used for this study. The sentiment analysis study revealed that out of those 76,979 tweets, more than half (n = 40,719 tweets (52.8%) had negative sentiments, 24,242 tweets (31.5%) had neutral sentiments, and 12,018 tweets (15.6%) had positive sentiments. Social media posts by Indians on the COVID-19 booster doses have focused on the feelings that younger people do not need vaccines and that vaccinations are unhealthy.
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spelling pubmed-96926462022-11-26 Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage SV, Praveen Lorenz, Jose Manuel Ittamalla, Rajesh Dhama, Kuldeep Chakraborty, Chiranjib Kumar, Daruri Venkata Srinivas Mohan, Thivyaa Vaccines (Basel) Brief Report This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the process of COVID-19 booster dose vaccinations. Understanding the emotions and concerns of the citizens regarding the health policy being implemented will assist the government, health policy officials, and policymakers implement the policy efficiently so that desired results can be achieved. Seventy-six thousand nine hundred seventy-nine tweets were used for this study. The sentiment analysis study revealed that out of those 76,979 tweets, more than half (n = 40,719 tweets (52.8%) had negative sentiments, 24,242 tweets (31.5%) had neutral sentiments, and 12,018 tweets (15.6%) had positive sentiments. Social media posts by Indians on the COVID-19 booster doses have focused on the feelings that younger people do not need vaccines and that vaccinations are unhealthy. MDPI 2022-11-15 /pmc/articles/PMC9692646/ /pubmed/36423024 http://dx.doi.org/10.3390/vaccines10111929 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Brief Report
SV, Praveen
Lorenz, Jose Manuel
Ittamalla, Rajesh
Dhama, Kuldeep
Chakraborty, Chiranjib
Kumar, Daruri Venkata Srinivas
Mohan, Thivyaa
Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title_full Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title_fullStr Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title_full_unstemmed Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title_short Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage
title_sort twitter-based sentiment analysis and topic modeling of social media posts using natural language processing, to understand people’s perspectives regarding covid-19 booster vaccine shots in india: crucial to expanding vaccination coverage
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692646/
https://www.ncbi.nlm.nih.gov/pubmed/36423024
http://dx.doi.org/10.3390/vaccines10111929
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