FinEntity: Entity-level Sentiment Classification for Financial Texts
Yixuan Tang Yi Yang Allen H Huang Andy Tam Justin Z Tang
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
\url{https://github.com/yixuantt/FinEntity}