LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
Summary
This paper presents a framework for Arabic financial sentiment analysis using LLMs, tailored for the Saudi market, integrating news and social media data to capture investor sentiment.
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# LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets Source: [https://arxiv.org/abs/2605.19714](https://arxiv.org/abs/2605.19714) [View PDF](https://arxiv.org/pdf/2605.19714) > Abstract:Investor sentiment shapes financial markets, yet modeling sentiment in Arabic financial contexts remains challenging due to linguistic complexity and limited resources\. We present an Arabic NLP framework for large\-scale financial sentiment analysis tailored to the Saudi market, integrating official financial news and social media to capture institutional and public investor sentiment\. The framework constructs a large Arabic financial corpus through a multi\-stage pipeline encompassing data collection, cleaning, deduplication, entity linking, and sentiment annotation\. Transformer\-based NER combined with a curated company lexicon links textual mentions to canonical company identifiers, with sentiment labels assigned using a five\-class scheme\. The resulting dataset of 84K samples supports company\-level sentiment aggregation and analysis of sentiment dynamics relative to stock market behavior on the Saudi Exchange\. Experimental results demonstrate reliable and scalable Arabic financial sentiment analysis\. ## Submission history From: Enrico Lopedoto \[[view email](https://arxiv.org/show-email/fa62f7bd/2605.19714)\] **\[v1\]**Tue, 19 May 2026 11:50:33 UTC \(563 KB\)
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