A Gemini-Based Model for Arabic Sentiment Analysis of Multi-Dialect Hotel Reviews: Ahasis Shared Task Submission
Year: 2025
Type: conference-paper
Abstract: This paper presents a sentiment analysis model tailored for Arabic dialects in the hospitality domain, developed for the Ahasis Shared Task.Leveraging the Gemini Pro 1.5 language model, we address the challenges posed by the diversity of Arabic dialects-specifically Saudi and Moroccan Darija.Our method utilized the official Ahasis dataset comprising 3,000 hotel reviews.Through iterative benchmarking, dialect labeling, sarcasm detection, and prompt engineering, we adapted Gemini Pro 1.5 for the task.The final model achieved an F1-score of 0.7361 and ranked 10th on the competition leaderboard.This work shows that prompt engineering and domain adaptation of LLMs can mitigate challenges of dialectal variation, sarcasm, and resource scarcity in Arabic sentiment classification.Our contribution lies in the integration of dialect-specific prompt tuning with real-time batch inference, avoiding retraining.This approach, validated across 3,000 competition samples and 700 internal benchmarks, establishes a novel template for Arabic-domain sentiment pipelines.
Author: Mohammed A. H. Lubbad
Institution: Erciyes University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140081641