# MAPROC at AHaSIS Shared Task: Few-Shot and Sentence Transformer for Sentiment Analysis of Arabic Hotel Reviews

arXiv: https://arxiv.org/abs/2511.15291 · HTML: https://mlsanity.com/paper/2511.15291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [LABR: A Large Scale Arabic Sentiment Analysis Benchmark](https://mlsanity.com/paper/1411.6718/) (2014)
- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)
- [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://mlsanity.com/paper/1906.01830/) (2019)
- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)
- [AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects](https://mlsanity.com/paper/2511.13335/) (2025)

## Abstract (arXiv)

Sentiment analysis of Arabic dialects presents significant challenges due to linguistic diversity and the scarcity of annotated data. This paper describes our approach to the AHaSIS shared task, which focuses on sentiment analysis on Arabic dialects in the hospitality domain. The dataset comprises hotel reviews written in Moroccan and Saudi dialects, and the objective is to classify the reviewers sentiment as positive, negative, or neutral. We employed the SetFit (Sentence Transformer Fine-tuning) framework, a data-efficient few-shot learning technique. On the official evaluation set, our system achieved an F1 of 73%, ranking 12th among 26 participants. This work highlights the potential of few-shot learning to address data scarcity in processing nuanced dialectal Arabic text within specialized domains like hotel reviews.