Year: 2025
Type: conference-paper
Abstract: Despite recent progress in large language models (LLMs), their performance on Arabic dialects remains underexplored, particularly in the context of sentiment analysis.This study presents a comparative evaluation of three LLMs, DeepSeek-R1, Qwen2.5, and LLaMA-3, on sentiment classification across Modern Standard Arabic (MSA), Saudi dialect and Darija.We construct a balanced sentiment dataset by translating and validating MSA hotel reviews into Saudi dialect and Darija.Using parameter-efficient fine-tuning (LoRA) and dialect-specific prompts, we assess each model under matched and mismatched prompting conditions.Experimental results show that Qwen2.5 achieves the highest macro F1 score of 79% on Darija input using MSA prompts, while DeepSeek performs best when prompted in the input dialect, reaching 71% on Saudi dialect.LLaMA-3 exhibits stable performance across prompt variations, with 75% macro F1 on Darija input under MSA prompting.Dialectaware prompting consistently improves classification accuracy, particularly for neutral and negative sentiment classes.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7122659114