LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
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Computer Science > Computation and Language
Title:LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
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.
| Comments: | Accepted at the 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7), co-located with LREC 2026, Palma de Mallorca, Spain, May 2026. ISBN: 978-2-493814-52-4 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.19714 [cs.CL] |
| (or arXiv:2605.19714v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19714
arXiv-issued DOI via DataCite (pending registration)
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