ArticleInternational journal of environmental research and public health2021
Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Identifying adverse reactions following COVID-19 vaccination in Korea using data from active surveillance: a text mining approach.Epidemiology and health · 2025Article
- How online public opinion evolves before and after policy adjustments in response to major public health emergencies.Frontiers in public health · 2025Article
- Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining.Vaccines · 2023Article
- [Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2023Article
- A case study of using natural language processing to extract consumer insights from tweets in American cities for public health crises.BMC public health · 2023Article
- Article
- Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts.Journal of medical Internet research · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis.Journal of medical Internet research · 2023Article
- Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.Cognitive computation · 2022Article
- Consumers' Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China.International journal of environmental research and public health · 2022Article
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
- Perceptions and attitudes towards Covid-19 vaccines: narratives from members of the UK public.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2022Article
- The Effect of Fear of Infection and Sufficient Vaccine Reservation Information on Rapid COVID-19 Vaccination in Japan: Evidence From a Retrospective Twitter Analysis.Journal of medical Internet research · 2022Article
- Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature.EClinicalMedicine · 2022Article
- Media Data and Vaccine Hesitancy: Scoping Review.JMIR infodemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people's lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for "coronavirus" and "vaccines" as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public's anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.
Indexed as
Identifiers
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.