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        <title>Sentiment Analysis of Nakba Oral Histories: A Critical Study of Large Language Models</title>
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            <title>Proceedings of the first International Workshop on Nakba Narratives as Language Resources</title>
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            <namePart type="given">Mustafa</namePart>
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    <abstract>This study explores the use of Large Language Models (LLMs), specifically ChatGPT, for sentiment analysis of Nakba oral histories, which document the experiences of Palestinian refugees. The study compares sentiment analysis results from full testimonies (average 2500 words) and their summarized versions (300 words). The findings reveal that summarization increased positive sentiment and decreased negative sentiment, suggesting that the process may highlight more hopeful themes while oversimplifying emotional complexities. The study highlights both the potential and limitations of using LLMs for analyzing sensitive, trauma-based narratives and calls for further research to improve sentiment analysis in such contexts.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis of Nakba Oral Histories: A Critical Study of Large Language Models
%A Ashqar, Huthaifa I.
%Y Jarrar, Mustafa
%Y Habash, Habash
%Y El-Haj, Mo
%S Proceedings of the first International Workshop on Nakba Narratives as Language Resources
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F ashqar-2025-sentiment
%X This study explores the use of Large Language Models (LLMs), specifically ChatGPT, for sentiment analysis of Nakba oral histories, which document the experiences of Palestinian refugees. The study compares sentiment analysis results from full testimonies (average 2500 words) and their summarized versions (300 words). The findings reveal that summarization increased positive sentiment and decreased negative sentiment, suggesting that the process may highlight more hopeful themes while oversimplifying emotional complexities. The study highlights both the potential and limitations of using LLMs for analyzing sensitive, trauma-based narratives and calls for further research to improve sentiment analysis in such contexts.
%U https://aclanthology.org/2025.nakbanlp-1.4/
%P 30-36

Markdown (Informal)

[Sentiment Analysis of Nakba Oral Histories: A Critical Study of Large Language Models](https://aclanthology.org/2025.nakbanlp-1.4/) (Ashqar, NakbaNLP 2025)

• Sentiment Analysis of Nakba Oral Histories: A Critical Study of Large Language Models (Ashqar, NakbaNLP 2025)

ACL

• Huthaifa I. Ashqar. 2025. Sentiment Analysis of Nakba Oral Histories: A Critical Study of Large Language Models. In Proceedings of the first International Workshop on Nakba Narratives as Language Resources, pages 30–36, Abu Dhabi. Association for Computational Linguistics.