ArticleKnowledge-based systems2021
TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.
Article in Knowledge-based systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Machine learning models to identify significant factors of panic buying situation.Scientific reports · 2025Article
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach.JMIR formative research · 2024Article
- Exploring post-COVID-19 health effects and features with advanced machine learning techniques.Scientific reports · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model.Frontiers in public health · 2024Article
- Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method.Scientific reports · 2022Article
- Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2022Article
- Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic.International journal of disaster risk reduction : IJDRR · 2022Article
- Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks.Computational intelligence and neuroscience · 2022Article
- COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.Informatics in medicine unlocked · 2022Article
- COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification.Computational intelligence and neuroscience · 2022Article
- Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing.JMIR public health and surveillance · 2021Article
- A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.Computers in biology and medicine · 2021Article
- The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study.JMIR medical informatics · 2021Article
- Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection.Computers in biology and medicine · 2021Article
- Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.Brain sciences · 2021Article
- Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.Briefings in bioinformatics · 2021Article
- COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders.Translational psychiatry · 2021Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.
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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.