[RANLP 2025] Join the Shared Task: Sentiment Analysis on Arabic Dialects in Hospitality
Ahasis Shared Task
Sentiment Across Multi-Dialectal Arabic: A Benchmark for Sentiment Analysis in the Hospitality Domain
We invite researchers, practitioners, and NLP enthusiasts to participate in the Sentiment Across Multi-Dialectal Arabic shared task, a challenge aimed at advancing sentiment analysis for Arabic dialects in the hospitality sector.
Arabic is one of the world’s most spoken languages, characterised by rich dialectal variation across different regions. These dialects significantly differ in syntax, vocabulary, and sentiment expression, making sentiment analysis a challenging NLP task. This task focuses on multi-dialectal sentiment detection in hotel reviews, where participants will classify sentiment as positive, neutral, or negative across multiple Arabic dialects, including Saudi, Moroccan, and Egyptian Arabic.
This shared task provides a high-quality multi-dialect parallel dataset, enabling participants to explore:
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.
Organising Team
Maram Alharbi, Lancaster University, UK
Salmane Chafik, Mohammed VI Polytechnic University, Morocco
Professor Ruslan Mitkov, Lancaster University, UK
Dr. Saad Ezzini, King Fahd University of Petroleum and Minerals, Saudi Arabia
Dr. Tharindo Ranasinghe, Lancaster University, UK
Dr. Hansi Hettiarachchi, Lancaster University, UK
For inquiries, please contact us at ahasi...@gmail.com