ArticleComputational and mathematical methods in medicine2021
Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 21 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
21 citing papers in PubMed, 109 citations in OpenAlex.
- Assessment of the Cultural Nuances in COVID-19 Vaccine Uptake Through a Comparative Analysis of English and Spanish Facebook Posts in Tarrant County, Texas: Longitudinal Study.Online journal of public health informatics · 2026Article
- A global twitter sentiment analysis model for COVID-vaccination.Scientific reports · 2026Article
- A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter.PeerJ. Computer science · 2025Article
- Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions.Journal of medical Internet research · 2024Article
- Aspect-based classification of vaccine misinformation: a spatiotemporal analysis using Twitter chatter.BMC public health · 2023Article
- Article
- COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.International journal of environmental research and public health · 2023Article
- Artificial Intelligence-Enabled Analysis of Statin-Related Topics and Sentiments on Social Media.JAMA network open · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- Enhanced sentiment analysis regarding COVID-19 news from global channels.Journal of computational social science · 2023Article
- Omicron virus emotions understanding system based on deep learning architecture.Journal of ambient intelligence and humanized computing · 2023Article
- Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.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
- Retracted: Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.Computational and mathematical methods in medicine · 2023Article
- A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines.Concurrency and computation : practice & experience · 2022Article
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
- Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature.EClinicalMedicine · 2022Article
- COVID-19 vaccination motivation and underlying believing processes: A comparison study between individuals with affective disorder and healthy controls.Frontiers in psychology · 2022Article
- Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.Computational intelligence and neuroscience · 2022Article
- Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-11-01Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.
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.