@inproceedings{alahmari-etal-2025-arabic,
    title = "{A}rabic-Centric Large Language Models for Dialectal {A}rabic Sentiment Analysis Task",
    author = "Alahmari, Salwa Saad  and
      Atwell, Eric  and
      Saadany, Hadeel  and
      Alsalka, Mohammad",
    editor = "Alharbi, Maram  and
      Chafik, Salmane  and
      Ezzini, Saad  and
      Mitkov, Ruslan  and
      Ranasinghe, Tharindu  and
      Hettiarachchi, Hansi",
    booktitle = "Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-ahasis.11/",
    pages = "69--75",
    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"
}