Year: 2025
Type: conference-paper
Abstract: 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-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 contributes new linguistic resources and empirical insights to support ongoing research in sentiment analysis for Arabic dialects
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140080090