AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects

TL;DR

This paper introduces a shared task on sentiment analysis for Arabic dialects in the hospitality domain, providing a curated dataset and evaluating system performance, highlighting progress and challenges in dialect-aware sentiment detection.

Contribution

It presents a new dataset and shared task for sentiment analysis in Arabic dialects, fostering research in dialect-specific NLP tools for real-world applications.

Findings

Top system achieved an F1 score of 0.81

Over 40 teams registered, 12 submitted systems

Demonstrates feasibility of dialect-aware sentiment analysis

Abstract

The hospitality industry in the Arab world increasingly relies on customer feedback to shape services, driving the need for advanced Arabic sentiment analysis tools. To address this challenge, the Sentiment Analysis on Arabic Dialects in the Hospitality Domain shared task focuses on Sentiment Detection in Arabic Dialects. This task leverages a multi-dialect, manually curated dataset derived from hotel reviews originally written in Modern Standard Arabic (MSA) and translated into Saudi and Moroccan (Darija) dialects. The dataset consists of 538 sentiment-balanced reviews spanning positive, neutral, and negative categories. Translations were validated by native speakers to ensure dialectal accuracy and sentiment preservation. This resource supports the development of dialect-aware NLP systems for real-world applications in customer experience analysis. More than 40 teams have registered for the shared task, with 12 submitting systems during the evaluation phase. The top-performing system achieved an F1 score of 0.81, demonstrating the feasibility and ongoing challenges of sentiment analysis across Arabic dialects.