This paper presents our contribution to the AraSentEval 2026 shared task, specifically for Subtask 1: Arabic Dialect Sentiment Analysis, hosted at the OSACT7 workshop during LREC 2026. The task focuses on classifying the sentiment (positive, negative, neutral) of text written in four major Arabic dialects: Moroccan, Egyptian, Jordanian, and Saudi. We addressed this by fine-tuning several pre-trained language models, including MARBERTv2 and CAMELBERT, on the provided Multi-Dialect-Sent (MDS-3) dataset. Our best-performing system MARBERTv2, achieved a Macro F1-score of 84.29% on the official test set, securing fourth place among 13 participating teams. Our findings underscore the value of leveraging large pre-trained models tailored to dialectal Arabic for improved sentiment classification in this under-resourced domain.

This paper presents our contribution to the AHASIS Shared Task at RANLP 2025, which focuses on sentiment analysis for Arabic dialects. While sentiment analysis has seen considerable progress in Modern Standard Arabic (MSA), the diversity and complexity of Arabic dialects pose unique challenges that remain underexplored. We address this by fine-tuning six pre-trained language models, including AraBERT, MARBERTv2, QARiB, and DarijaBERT, on a sentiment-labeled dataset comprising hotel reviews written in Saudi and Moroccan (Darija) dialects. Our experiments evaluate the models’ performance on both combined and individual dialect datasets. MARBERTv2 achieved the highest performance with an F1-score of 79% on the test set, securing third place among 14 participants. We further analyze the effectiveness of each model across dialects, demonstrating the importance of dialect-aware pretraining for Arabic sentiment analysis. Our findings highlight the value of leveraging large pre-trained models tailored to dialectal Arabic for improved sentiment classification.