Entity-level fine-grained sentiment analysis in the financial domain is a crucial subtask of sentiment analysis and currently faces numerous challenges. The primary challenge stems from the lack of high-quality and large-scale annotated corpora specifically designed for financial text sentiment analysis, which in turn limits the availability of data necessary for developing effective text processing techniques. Recent advancements in large language models (LLMs) have yielded remarkable performance in natural language processing tasks, primarily centered around language pattern matching. In this paper, we propose a novel and extensive Chinese fine-grained financial sentiment analysis dataset, FinChina SA, for enterprise early warning. We thoroughly evaluate and experiment with well-known existing open-source LLMs using our dataset. We firmly believe that our dataset will serve as a valuable resource to advance the exploration of real-world financial sentiment analysis tasks, which should be the focus of future research. The FinChina SA dataset is publicly available at https://github.com/YerayL/FinChina-SA

翻译：实体级细粒度情感分析是金融领域情感分析的重要子任务，当前面临诸多挑战。主要挑战在于缺乏专为金融文本情感分析设计的高质量大规模标注语料库，这限制了开发有效文本处理技术所需数据的可用性。近年来，大语言模型（LLMs）在自然语言处理任务中取得了显著成果，主要围绕语言模式匹配展开。本文提出了一种新颖且广泛的中文细粒度金融情感分析数据集FinChina SA，用于企业预警。我们利用该数据集对现有知名的开源大语言模型进行了全面评估与实验。我们坚信，该数据集将作为推动真实世界金融情感分析任务探索的宝贵资源，这应是未来研究的重点方向。FinChina SA数据集已公开于https://github.com/YerayL/FinChina-SA