构建首个面向沙特市场的阿拉伯语金融情感分析框架，融合新闻与社交媒体数据。
LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
- 基于Transformer的命名实体识别与公司词典匹配，实现文本提及到公司标识的精准链接。
- 构建包含8.4万样本的阿拉伯语金融语料库，支持公司级情感聚合与股市行为关联分析。
- 首次系统性解决阿拉伯语金融情感分析难题，适合关注中东市场AI应用的研究者。
投资者情绪塑造金融市场，但受语言复杂性和资源有限影响，阿拉伯语金融语境下的情感建模仍具挑战。本文提出一个面向沙特市场的阿拉伯语NLP框架，整合官方财经新闻与社交媒体数据，捕捉机构与公众投资者情绪。通过多阶段流程（数据收集、清洗、去重、实体链接、情感标注）构建大规模阿拉伯语金融语料库。采用基于Transformer的命名实体识别技术结合定制公司词典，将文本提及映射至标准公司标识，并使用五分类体系赋予情感标签。最终数据集含84,000个样本，支持公司级情感聚合及与沙特证券交易所股价行为的动态关联分析。实验结果表明该框架具备可靠性和可扩展性。
原文摘要 · Abstract (English)
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.
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