# AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects

arXiv: https://arxiv.org/abs/2511.13335 · HTML: https://mlsanity.com/paper/2511.13335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews](https://mlsanity.com/paper/2010.13814/) (2020)
- [SaudiBERT: A Large Language Model Pretrained on Saudi Dialect Corpora](https://mlsanity.com/paper/2405.06239/) (2024)
- [The Impact of Indirect Machine Translation on Sentiment Classification](https://mlsanity.com/paper/2008.11257/) (2020)

## Built on by (most cited first, 1 of 1)

- [MAPROC at AHaSIS Shared Task: Few-Shot and Sentence Transformer for Sentiment Analysis of Arabic Hotel Reviews](https://mlsanity.com/paper/2511.15291/) (2025)

## Abstract (arXiv)

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.