A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis
As a valuable tool for comprehending the emotions and perspectives of individuals, the significance of sentiment analysis has risen as a result of the growing of user-generated Arabic content online. Aspect-based sentiment analysis (ABSA) has recently gained significant attention and has become one...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10872945/ |
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| author | Dalal Alqusair Mounira Taileb and Hassanin Al-Barhamtoshy |
| author_facet | Dalal Alqusair Mounira Taileb and Hassanin Al-Barhamtoshy |
| author_sort | Dalal Alqusair |
| collection | DOAJ |
| description | As a valuable tool for comprehending the emotions and perspectives of individuals, the significance of sentiment analysis has risen as a result of the growing of user-generated Arabic content online. Aspect-based sentiment analysis (ABSA) has recently gained significant attention and has become one of the most popular research points. The main objective of ABSA is to extract the aspects and identify their corresponding sentiment polarity from a provided review or text. Compared to generic sentiment analysis, the outcome provides more in-depth information. This paper aims to explore the deep learning (DL) methods employed in Arabic ABSA, and provides a new taxonomy that organizes various ABSA studies depending on the number of tasks processed. Additionally, the models proposed for Arabic ABSA and their contributions and limitations are discussed and summarized to identify gaps in the field. Furthermore, Arabic datasets for ABSA are reviewed as well. Specifically, this article analyzes studies published between 2019 and April 2024. In addition to ascertaining potential future directions that would encourage researchers to contribute to Arabic ABSA studies and generate more effective algorithms. |
| format | Article |
| id | doaj-art-4a291c7882c24fc48b28131d5f9af11c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4a291c7882c24fc48b28131d5f9af11c2025-02-12T00:01:38ZengIEEEIEEE Access2169-35362025-01-0113253502536810.1109/ACCESS.2025.353926310872945A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment AnalysisDalal Alqusair0https://orcid.org/0009-0009-5885-0474Mounira Taileb1https://orcid.org/0000-0002-5195-7801and Hassanin Al-Barhamtoshy2https://orcid.org/0000-0003-3915-9513Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaAs a valuable tool for comprehending the emotions and perspectives of individuals, the significance of sentiment analysis has risen as a result of the growing of user-generated Arabic content online. Aspect-based sentiment analysis (ABSA) has recently gained significant attention and has become one of the most popular research points. The main objective of ABSA is to extract the aspects and identify their corresponding sentiment polarity from a provided review or text. Compared to generic sentiment analysis, the outcome provides more in-depth information. This paper aims to explore the deep learning (DL) methods employed in Arabic ABSA, and provides a new taxonomy that organizes various ABSA studies depending on the number of tasks processed. Additionally, the models proposed for Arabic ABSA and their contributions and limitations are discussed and summarized to identify gaps in the field. Furthermore, Arabic datasets for ABSA are reviewed as well. Specifically, this article analyzes studies published between 2019 and April 2024. In addition to ascertaining potential future directions that would encourage researchers to contribute to Arabic ABSA studies and generate more effective algorithms.https://ieeexplore.ieee.org/document/10872945/Arabic ABSAaspect leveldeep learningopinion miningsentiment analysis |
| spellingShingle | Dalal Alqusair Mounira Taileb and Hassanin Al-Barhamtoshy A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis IEEE Access Arabic ABSA aspect level deep learning opinion mining sentiment analysis |
| title | A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis |
| title_full | A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis |
| title_fullStr | A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis |
| title_full_unstemmed | A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis |
| title_short | A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis |
| title_sort | survey of deep learning techniques for arabic aspect based sentiment analysis |
| topic | Arabic ABSA aspect level deep learning opinion mining sentiment analysis |
| url | https://ieeexplore.ieee.org/document/10872945/ |
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