用大模型和FinBERT分析财经文本情绪，效果接近微调模型。
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT
- 用提示工程+少量样本提升金融文本情绪分类准确率
- GPT-4o用少量金融文本示例就达到微调FinBERT水平
- 适合想低成本做金融情绪分析的研究者和从业者
金融情绪分析对评估市场情绪和做出明智投资决策至关重要。大型语言模型（如BERT及其金融版FinBERT）显著提升了情绪分析能力。本文研究了LLMs与FinBERT在新闻、财务报告及公司公告中的应用，重点探讨了零样本与少样本提示工程的优势。实验表明，GPT-4o在仅使用少量金融文本示例的情况下，其表现可媲美经过充分微调的FinBERT，展示了无需大规模训练即可实现高精度金融情绪分析的可行性。
原文摘要 · Abstract (English)
Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models (LLMs) such as BERT and its financial variant, FinBERT, has notably enhanced sentiment analysis capabilities. This paper investigates the application of LLMs and FinBERT for FSA, comparing their performance on news articles, financial reports and company announcements. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy. Experimental results indicate that GPT-4o, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field.
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