Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and fine-tuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.

翻译：情感分析是从金融文章、新闻和社交媒体中洞察信息、塑造我们对市场走势理解的重要工具。尽管大语言模型在金融自然语言处理领域展现出卓越能力，但在准确理解数值和把握金融语境方面仍存在不足，从而限制了其在金融情感预测中的有效性。本文提出一种简洁高效的指令微调方法来解决上述问题。通过将少量监督式金融情感分析数据转化为指令数据，并采用此方法对通用大语言模型进行微调，我们在金融情感分析领域取得了显著突破。实验表明，我们的方法在数值理解与语境理解至关重要的场景中，不仅超越现有最优监督式情感分析模型，还优于ChatGPT和LLaMA等广泛使用的通用大语言模型。