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.
@article{arxiv.2605.19714,
title = {LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets},
author = {Mona H. Albaqawi and Eman M. Albalkhi and Joud A. Albaiti and Enrico Lopedoto},
journal= {arXiv preprint arXiv:2605.19714},
year = {2026}
}
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