ArticleWorld wide web2022
Sentiment analysis and topic modeling for COVID-19 vaccine discussions.
Article in World wide web, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
The trial behind it
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Who cites it
23 citing papers in PubMed, 117 citations in OpenAlex.
- Health information dynamics and audience responses to avian influenza on YouTube: Implications for digital public health communication.PNAS nexus · 2026Article
- A novel lexicon dictionary and CNN-LSTM employed hybrid approach for sentiment detection of COVID-19 vaccines.Scientific reports · 2026Article
- BERTopic-driven term extraction from biomedical texts toward ontology population: evaluating vaccine ontology with Plotkin's vaccines corpus.Journal of biomedical semantics · 2026Article
- Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026Article
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Optimized hierarchical CLSTM model for sentiment classification of tweets using boosted killer whale predation strategy.Scientific reports · 2025Article
- Article
- Unveiling public perceptions at the beginning of lockdown: an application of structural topic modeling and sentiment analysis in the UK and India.BMC public health · 2024Article
- Social support and audience engagement of lupus-related posts on social networking sites in China.Heliyon · 2024Review
- Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.Behavioral sciences (Basel, Switzerland) · 2024Article
- Design and implementation of the international news commentary Data Intelligent Processing System.PeerJ. Computer science · 2024Article
- User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults.Public health in practice (Oxford, England) · 2023Article
- Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets.Journal of medical Internet research · 2023Article
- Topic sentiment analysis based on deep neural network using document embedding technique.The Journal of supercomputing · 2023Article
- Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms.Healthcare (Basel, Switzerland) · 2023Article
- Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic.PeerJ. Computer science · 2023Article
- Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data.Frontiers in public health · 2023Article
- EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation.World wide web · 2022Article
- Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work.JMIR formative research · 2022Article
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people's opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.
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What OpenQuestion holds
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.