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.
About the Task 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:
1. Dialect-Specific Sentiment Detection – Understanding how sentiment varies across dialects. 2. Cross-Linguistic Sentiment Analysis – Investigating sentiment preservation across dialects. 3. 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
1. Register for the task via https://ahasis-42267.web.app/ 2. Download the dataset and baseline system. 3. Develop and test your sentiment analysis model. 4. 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 ahasis.task@gmail.com