LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
This work addresses the lack of Arabic financial sentiment resources for the Saudi market, but the contribution is incremental as it applies existing methods to a new domain.
The paper presents an Arabic NLP framework for financial sentiment analysis tailored to the Saudi market, integrating news and social media to capture investor sentiment. The framework constructs a dataset of 84K samples and demonstrates reliable and scalable sentiment analysis.
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.