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            <title>Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects</title>
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            <namePart type="given">Salmane</namePart>
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    <abstract>Arabic exhibits a rich and intricate linguistic landscape, with Modern Standard Arabic (MSA) serving as the formal written and spoken medium, alongside a wide variety of regional dialects used in everyday communication. These dialects vary considerably in syntax, vocabulary, phonology, and meaning, presenting significant challenges for natural language processing (NLP). The complexity is particularly pronounced in sentiment analysis, where emotional expressions and idiomatic phrases differ markedly across regions, hindering consistent and accurate sentiment detection. This paper describes our submission to the Ahasis Shared Task: A Benchmark for Arabic Sentiment Analysis in the hospitality domain. This shared task focuses on advancing sentiment analysis techniques for Arabic dialects in the hotel domain. Our proposed approach achieved an F1 score of 0.88 % on the internal test set (split from the original training data), and 79.16% on the official hidden test set of the shared task. This performance secured our team second place in the Ahasis Shared Task.</abstract>
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%0 Conference Proceedings
%T Fine-tuning AraBert model for arabic sentiment detection
%A Jaballah, Mustapha
%A Ghoul, Dhaou
%A Mars, Ammar
%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 jaballah-etal-2025-fine
%X Arabic exhibits a rich and intricate linguistic landscape, with Modern Standard Arabic (MSA) serving as the formal written and spoken medium, alongside a wide variety of regional dialects used in everyday communication. These dialects vary considerably in syntax, vocabulary, phonology, and meaning, presenting significant challenges for natural language processing (NLP). The complexity is particularly pronounced in sentiment analysis, where emotional expressions and idiomatic phrases differ markedly across regions, hindering consistent and accurate sentiment detection. This paper describes our submission to the Ahasis Shared Task: A Benchmark for Arabic Sentiment Analysis in the hospitality domain. This shared task focuses on advancing sentiment analysis techniques for Arabic dialects in the hotel domain. Our proposed approach achieved an F1 score of 0.88 % on the internal test set (split from the original training data), and 79.16% on the official hidden test set of the shared task. This performance secured our team second place in the Ahasis Shared Task.
%U https://aclanthology.org/2025.ranlp-ahasis.3/
%P 14-23

Markdown (Informal)

[Fine-tuning AraBert model for arabic sentiment detection](https://aclanthology.org/2025.ranlp-ahasis.3/) (Jaballah et al., RANLP 2025)

• Fine-tuning AraBert model for arabic sentiment detection (Jaballah et al., RANLP 2025)

ACL

• Mustapha Jaballah, Dhaou Ghoul, and Ammar Mars. 2025. Fine-tuning AraBert model for arabic sentiment detection. In Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects, pages 14–23, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.