ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning
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 t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-10-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1507.pdf |
| _version_ | 1827781580823724032 |
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| author | Sarah Alhumoud Asma Al Wazrah Laila Alhussain Lama Alrushud Atheer Aldosari Reema Nasser Altammami Njood Almukirsh Hind Alharbi Wejdan Alshahrani |
| author_facet | Sarah Alhumoud Asma Al Wazrah Laila Alhussain Lama Alrushud Atheer Aldosari Reema Nasser Altammami Njood Almukirsh Hind Alharbi Wejdan Alshahrani |
| author_sort | Sarah Alhumoud |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T15:20:42Z |
| format | Article |
| id | doaj.art-006fad8cdc074525991eb29a27039e69 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-11T15:20:42Z |
| publishDate | 2023-10-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-006fad8cdc074525991eb29a27039e692023-10-28T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922023-10-019e150710.7717/peerj-cs.1507ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learningSarah AlhumoudAsma Al WazrahLaila AlhussainLama AlrushudAtheer AldosariReema Nasser AltammamiNjood AlmukirshHind AlharbiWejdan AlshahraniCOVID-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.https://peerj.com/articles/cs-1507.pdfDeep learningMachine learningText miningNatural language processingSentiment analysisCOVID-19 vaccine |
| spellingShingle | Sarah Alhumoud Asma Al Wazrah Laila Alhussain Lama Alrushud Atheer Aldosari Reema Nasser Altammami Njood Almukirsh Hind Alharbi Wejdan Alshahrani ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning PeerJ Computer Science Deep learning Machine learning Text mining Natural language processing Sentiment analysis COVID-19 vaccine |
| title | ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning |
| title_full | ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning |
| title_fullStr | ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning |
| title_full_unstemmed | ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning |
| title_short | ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning |
| title_sort | asavact arabic sentiment analysis for vaccine related covid 19 tweets using deep learning |
| topic | Deep learning Machine learning Text mining Natural language processing Sentiment analysis COVID-19 vaccine |
| url | https://peerj.com/articles/cs-1507.pdf |
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