Investor sentiment plays a crucial role in shaping financial markets, yet modeling sentiment in Arabic financial contexts has remained a persistent challenge due to linguistic complexities and a lack of specialized resources. A new Arabic NLP framework has been presented, specifically tailored for large-scale financial sentiment analysis within the Saudi market, bridging the gap between institutional news and public sentiment.
The framework constructs an extensive Arabic financial corpus through a sophisticated multi-stage pipeline. This pipeline encompasses data collection, rigorous cleaning, deduplication, entity linking, and sentiment annotation. To address the nuances of the Saudi market, the researchers integrated Transformer-based Named Entity Recognition (NER) with a curated company lexicon. This combination allows for precise linking of textual mentions to canonical company identifiers, ensuring data accuracy across 84,000 samples.
Sentiment labels are assigned using a detailed five-class scheme, facilitating granular company-level sentiment aggregation. Furthermore, the framework enables the analysis of sentiment dynamics relative to actual stock market behavior on the Saudi Exchange. The experimental results validate that this approach offers a reliable and scalable solution for Arabic financial sentiment analysis, providing a robust foundation for understanding the interplay between public discourse and market performance.
