ArticlePeerJ. Computer science2023
ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach.JMIR infodemiology · 2025Article
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- Transformer-based ensemble model for dialectal Arabic sentiment classification.PeerJ. Computer science · 2025Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the pandemic. One of the most debated topics in that regard is the vaccine. People are divided mainly into two groups, pro-vaccine and anti-vaccine. This article aims to explore Arabic Sentiment Analysis for Vaccine-Related COVID-19 Tweets (ASAVACT) to quantify sentiment polarity shared publicly, and it is considered the first and the largest human-annotated dataset in Arabic. The analysis is done using state-of-the-art deep learning models that proved superiority in the field of language processing and analysis. The models are the stacked gated recurrent unit (SGRU), the stacked bidirectional gated recurrent unit (SBi-GRU), and the ensemble architecture of SGRU, SBi-GRU, and AraBERT. Additionally, this article presents the largest Arabic Twitter corpus on COVID-19 vaccination, with 32,476 annotated Tweets. The results show that the ensemble model outperformed other singular models with at least 7% accuracy enhancement.
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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.