Arabic-Centric Large Language Models for Dialectal Arabic Sentiment Analysis Task

This paper presents a study on sentiment analysis of Dialectal Arabic (DA), with a particular focus on Saudi and Moroccan (Darija) dialects within the hospitality domain. We introduce a novel dataset comprising 698 Saudi Arabian proverbs annotated with sentiment polarity labels—Positive, Negative, and Neutral—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-DASA 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 contributes new linguistic resources and empirical insights to support ongoing research in sentiment analysis for Arabic dialects.