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        <title>Dialect-Aware Sentiment Analysis for Ahasis Challenge</title>
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            <title>Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects</title>
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            <namePart type="given">Maram</namePart>
            <namePart type="family">Alharbi</namePart>
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        <name type="personal">
            <namePart type="given">Salmane</namePart>
            <namePart type="family">Chafik</namePart>
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            <namePart type="given">Saad</namePart>
            <namePart type="family">Ezzini</namePart>
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            <namePart type="family">Mitkov</namePart>
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            <namePart type="family">Ranasinghe</namePart>
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    <abstract>This paper presents our approach to Arabic sentiment analysis with a specific focus on dialect-awareness for Saudi and Moroccan (Darija) dialectal variants. We develop a system that achieves a macro F1 score of 77% on the test set, demonstrating effective generalization across these dialect variations. Our approach leverages a pre-trained Arabic language model (Qarib) with custom dialect-specific embeddings and preprocessing techniques tailored to each dialect. The results show a significant improvement over baseline models that do not incorporate dialect information, with an absolute gain of 5% in F1 score over the equivalent non-dialect-aware model. Our analysis further reveals distinct sentiment expression patterns between Saudi and Darija dialects, highlighting the importance of dialect-aware approaches for Arabic sentiment analysis.</abstract>
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%0 Conference Proceedings
%T Dialect-Aware Sentiment Analysis for Ahasis Challenge
%A Chouikhi, Hasna
%A Aloui, Manel
%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 chouikhi-aloui-2025-dialect
%X This paper presents our approach to Arabic sentiment analysis with a specific focus on dialect-awareness for Saudi and Moroccan (Darija) dialectal variants. We develop a system that achieves a macro F1 score of 77% on the test set, demonstrating effective generalization across these dialect variations. Our approach leverages a pre-trained Arabic language model (Qarib) with custom dialect-specific embeddings and preprocessing techniques tailored to each dialect. The results show a significant improvement over baseline models that do not incorporate dialect information, with an absolute gain of 5% in F1 score over the equivalent non-dialect-aware model. Our analysis further reveals distinct sentiment expression patterns between Saudi and Darija dialects, highlighting the importance of dialect-aware approaches for Arabic sentiment analysis.
%U https://aclanthology.org/2025.ranlp-ahasis.7/
%P 40-45

Markdown (Informal)

[Dialect-Aware Sentiment Analysis for Ahasis Challenge](https://aclanthology.org/2025.ranlp-ahasis.7/) (Chouikhi & Aloui, RANLP 2025)

• Dialect-Aware Sentiment Analysis for Ahasis Challenge (Chouikhi & Aloui, RANLP 2025)

ACL

• Hasna Chouikhi and Manel Aloui. 2025. Dialect-Aware Sentiment Analysis for Ahasis Challenge. In Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects, pages 40–45, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.