Sentiment Analysis on Arabic Dialects in Hospitality Domain

Participate in the Sentiment Across Multi-Dialectal Arabic shared task, advancing sentiment analysis for Arabic dialects in the hospitality sector.

Join the Sentiment Across Multi-Dialectal Arabic shared task, a challenge aimed at advancing sentiment analysis for Arabic dialects in the hospitality sector.

About the Task:

• Dialect-Specific Sentiment Detection – Understanding how sentiment varies across dialects.
• Cross-Linguistic Sentiment Analysis – Investigating sentiment preservation across dialects.
• Benchmarking on Multi-Dialect Data – Evaluating models on a standardised Arabic dialect dataset.

Dataset Overview:

• Hotel reviews across multiple Arabic dialects.
• Balanced sentiment distribution (positive, neutral, negative).
• Multi-Dialect Parallel Dataset – Each review is available in multiple dialects, allowing for cross-linguistic comparison.

Evaluation Metrics:

• Primary Metric: F1-Score.
• Additional Analysis: Comparison of sentiment accuracy across dialects.

Baseline System:

• Pre-trained BERT-based model (AraBERT) fine-tuned on MSA and Arabic dialect data.
• Participants are encouraged to improve upon the baseline model with their own techniques and use LLMs.

Why Participate?

• Contribute to Arabic NLP Research – Help advance sentiment analysis for Arabic dialects.
• Gain Access to a High-Quality Dataset – A unique multi-dialect benchmark for future research.
• Collaborate with the NLP Community – Engage with leading researchers and practitioners.
• Showcase Your Work – High-performing models may be featured in a post-task publication.

Timeline:

• Training data ready – April 15, 2024
• Test Evaluation starts – April 27, 2025
• Test Evaluation end – May 10, 2025
• Paper submission due – May 16, 2025
• Notification to authors – May 31, 2025
• Shared task presentation co-located with RANLP 2025 – September 11 and September 12, 2025

How to Participate?

• Register for the task via https://ahasis-42267.web.app/
• Download the dataset and baseline system.
• Develop and test your sentiment analysis model.
• Submit your results for evaluation.

Tags: Sentiment Analysis, Arabic Dialects, Hospitality Domain, NLP, Multi-Dialectal Arabic, Shared Task, RANLP 2025