In the financial domain, conducting entity-level sentiment analysis is
crucial for accurately assessing the sentiment directed toward a specific
financial entity. To our knowledge, no publicly available dataset currently
exists for this purpose. In this work, we introduce an entity-level sentiment
classification dataset, called \textbf{FinEntity}, that annotates financial
entity spans and their sentiment (positive, neutral, and negative) in financial
news. We document the dataset construction process in the paper. Additionally,
we benchmark several pre-trained models (BERT, FinBERT, etc.) and ChatGPT on
entity-level sentiment classification. In a case study, we demonstrate the
practical utility of using FinEntity in monitoring cryptocurrency markets. The
data and code of FinEntity is available at
https://github.com/yixuantt/FinEntity

我们介绍了一个名为 FinEntity 的实体级情感分类数据集，用于金融领域中指向特定金融实体的情感分析。我们在论文中记录了数据集构建过程，并在几个预训练模型（BERT、FinBERT 等）和 ChatGPT 上进行了实体级情感分类的基准测试。我们通过一个案例研究展示了使用 FinEntity 在监测加密货币市场中的实际应用价值。FinEntity 的数据和代码可在 https://github.com/yixuantt/FinEntity 获取。