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        <title>Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task</title>
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            <title>Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects</title>
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    <abstract>This paper presents a study on sentiment anal- ysis of Dialectal Arabic (DA), with a particu- lar focus on Saudi and Moroccan (Darija) di- alects within the hospitality domain. We in- troduce a novel dataset comprising 698 Saudi Arabian proverbs annotated with sentiment polarity labels—Positive, Negative, and Neu- tral—collected from five major Saudi dialect regions: Najdi, Hijazi, Shamali, Janoubi, and Sharqawi. In addition to this, we used customer reviews for fine-tuning the CAMeLBERT-DA- SA model, which achieved a 75% F1 score in sentiment classification. To further evaluate the robustness of Arabic-centric models, we assessed the performance of three open-source large language models—Allam, ACeGPT, and Jais—in a zero-shot setting using the Ahasis shared task test set. Our results highlight the effectiveness of domain-specific fine-tuning in improving sentiment analysis performance and demonstrate the potential of Arabic-centric LLMs in zero-shot scenarios. This work con- tributes new linguistic resources and empirical insights to support ongoing research in senti- ment analysis for Arabic dialect</abstract>
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%0 Conference Proceedings
%T Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task
%A Alahmari, Salwa Saad
%A Atwell, Eric
%A Saadany, Hadeel
%A Alsalka, Mohammad
%Y Alharbi, Maram
%Y Chafik, Salmane
%Y Ezzini, Saad
%Y Mitkov, Ruslan
%Y Ranasinghe, Tharindu
%Y Hettiarachchi, Hansi
%S Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F alahmari-etal-2025-arabic
%X This paper presents a study on sentiment anal- ysis of Dialectal Arabic (DA), with a particu- lar focus on Saudi and Moroccan (Darija) di- alects within the hospitality domain. We in- troduce a novel dataset comprising 698 Saudi Arabian proverbs annotated with sentiment polarity labels—Positive, Negative, and Neu- tral—collected from five major Saudi dialect regions: Najdi, Hijazi, Shamali, Janoubi, and Sharqawi. In addition to this, we used customer reviews for fine-tuning the CAMeLBERT-DA- SA model, which achieved a 75% F1 score in sentiment classification. To further evaluate the robustness of Arabic-centric models, we assessed the performance of three open-source large language models—Allam, ACeGPT, and Jais—in a zero-shot setting using the Ahasis shared task test set. Our results highlight the effectiveness of domain-specific fine-tuning in improving sentiment analysis performance and demonstrate the potential of Arabic-centric LLMs in zero-shot scenarios. This work con- tributes new linguistic resources and empirical insights to support ongoing research in senti- ment analysis for Arabic dialect
%U https://aclanthology.org/2025.ranlp-ahasis.11/
%P 69-75

Markdown (Informal)

[Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task](https://aclanthology.org/2025.ranlp-ahasis.11/) (Alahmari et al., RANLP 2025)

• Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task (Alahmari et al., RANLP 2025)

ACL

• Salwa Saad Alahmari, Eric Atwell, Hadeel Saadany, and Mohammad Alsalka. 2025. Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task. In Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects, pages 69–75, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.