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        <title>mucAI at Ahasis Shared Task: Sentiment Analysis with Adaptive Few Shot Prompting</title>
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            <title>Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects</title>
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            <namePart type="given">Maram</namePart>
            <namePart type="family">Alharbi</namePart>
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            <namePart type="given">Salmane</namePart>
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        <name type="personal">
            <namePart type="given">Saad</namePart>
            <namePart type="family">Ezzini</namePart>
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    <abstract>Sentiment Analysis is a crucial task in Natural Language Processing (NLP) focused on identifying and categorizing emotional tones or opinions within text. For Arabic customer reviews, sentiment analysis is particularly challenging. The language’s rich diversity, with numerous regional dialects differing significantly from Modern Standard Arabic (MSA) and each other in lexicon, syntax, and sentiment expression, complicates consistent performance across dialects. In this paper, we present our approach, submitted to the AHASIS Shared Task 2025, focusing on sentiment analysis for Arabic dialects in the hotel domain. Our method leverages the capabilities of GPT-4o through adaptive few-shot prompting technique, where similar contextual examples are dynamically selected for each review using a k-Nearest Neighbors (kNN) search over train embeddings from a fine-tuned encoder model. This approach tailors the prompt to each specific instance, enhancing classification performance over minority class. Our submission achieved an F1-score of 76.0% on the official test set, showing stronger performance for the Saudi dialect compared to Darija.</abstract>
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            <start>54</start>
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%0 Conference Proceedings
%T mucAI at Ahasis Shared Task: Sentiment Analysis with Adaptive Few Shot Prompting
%A Abdou, Ahmed Mohamed Abdelaal
%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 abdou-2025-mucai-ahasis
%X Sentiment Analysis is a crucial task in Natural Language Processing (NLP) focused on identifying and categorizing emotional tones or opinions within text. For Arabic customer reviews, sentiment analysis is particularly challenging. The language’s rich diversity, with numerous regional dialects differing significantly from Modern Standard Arabic (MSA) and each other in lexicon, syntax, and sentiment expression, complicates consistent performance across dialects. In this paper, we present our approach, submitted to the AHASIS Shared Task 2025, focusing on sentiment analysis for Arabic dialects in the hotel domain. Our method leverages the capabilities of GPT-4o through adaptive few-shot prompting technique, where similar contextual examples are dynamically selected for each review using a k-Nearest Neighbors (kNN) search over train embeddings from a fine-tuned encoder model. This approach tailors the prompt to each specific instance, enhancing classification performance over minority class. Our submission achieved an F1-score of 76.0% on the official test set, showing stronger performance for the Saudi dialect compared to Darija.
%U https://aclanthology.org/2025.ranlp-ahasis.9/
%P 54-61

Markdown (Informal)

[mucAI at Ahasis Shared Task: Sentiment Analysis with Adaptive Few Shot Prompting](https://aclanthology.org/2025.ranlp-ahasis.9/) (Abdou, RANLP 2025)

• mucAI at Ahasis Shared Task: Sentiment Analysis with Adaptive Few Shot Prompting (Abdou, RANLP 2025)

ACL

• Ahmed Mohamed Abdelaal Abdou. 2025. mucAI at Ahasis Shared Task: Sentiment Analysis with Adaptive Few Shot Prompting. In Proceedings of the Shared Task on Sentiment Analysis for Arabic Dialects, pages 54–61, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.