ArticleVaccines2022
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.
Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 28 citations in OpenAlex.
- Public feedback analysis on multi-stage emergency management policies using BERTopic-SKEP integrated model.Scientific reports · 2025Article
- A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.Scientific reports · 2024Article
- Fight Against the Mandatory COVID-19 Immunity Passport on Twitter: Natural Language Processing Study.Journal of medical Internet research · 2023Article
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- COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India.Social network analysis and mining · 2023Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.