@inproceedings{al-hadhrami-etal-2025-lab17,
title = "Lab17 @ Ahasis Shared Task 2025: Fine-Tuning and Prompting techniques for Sentiment Analysis of Saudi and {D}arija Dialects",
author = "Al Hadhrami, Al Mukhtar and
Al Mahrouqi, Firas and
Al Shaaili, Mohammed and
Mulki, Hala",
editor = "Alharbi, Maram and
Chafik, Salmane and
Ezzini, Saad and
Mitkov, Ruslan and
Ranasinghe, Tharindu and
Hettiarachchi, Hansi",
booktitle = "Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-ahasis.6/",
pages = "35--39",
abstract = "In this paper, we describe our contribution in Ahasis shared task: Sentiment analysis on Arabic Dialects in the Hospitality Domain. Through the presented framework, we explored using two learning strategies tailored to a Large Language Model (LLM) and Transformer-based model variants. While few-shot prompting was used with GPT-4o, fine-tuning was adopted once to refine the essential MARBERT model on the Ahasis dataset and then to utilize a MARBERT variant model, SODA-BERT, that was pretrained on an Omani sentiment dataset and later evaluated with the shared task data."
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%0 Conference Proceedings
%T Lab17 @ Ahasis Shared Task 2025: Fine-Tuning and Prompting techniques for Sentiment Analysis of Saudi and Darija Dialects
%A Al Hadhrami, Al Mukhtar
%A Al Mahrouqi, Firas
%A Al Shaaili, Mohammed
%A Mulki, Hala
%Y Alharbi, Maram
%Y Chafik, Salmane
%Y Ezzini, Saad
%Y Mitkov, Ruslan
%Y Ranasinghe, Tharindu
%Y Hettiarachchi, Hansi
%S Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F al-hadhrami-etal-2025-lab17
%X In this paper, we describe our contribution in Ahasis shared task: Sentiment analysis on Arabic Dialects in the Hospitality Domain. Through the presented framework, we explored using two learning strategies tailored to a Large Language Model (LLM) and Transformer-based model variants. While few-shot prompting was used with GPT-4o, fine-tuning was adopted once to refine the essential MARBERT model on the Ahasis dataset and then to utilize a MARBERT variant model, SODA-BERT, that was pretrained on an Omani sentiment dataset and later evaluated with the shared task data.
%U https://aclanthology.org/2025.ranlp-ahasis.6/
%P 35-39
Markdown (Informal)
[Lab17 @ Ahasis Shared Task 2025: Fine-Tuning and Prompting techniques for Sentiment Analysis of Saudi and Darija Dialects](https://aclanthology.org/2025.ranlp-ahasis.6/) (Al Hadhrami et al., RANLP 2025)
ACL