FinEntity: Entity-level Sentiment Classification for Financial Texts
This work addresses the problem of accurately assessing sentiment toward specific financial entities for researchers and practitioners in finance, but it is incremental as it primarily introduces a new dataset rather than a novel method.
The authors tackled the lack of a publicly available dataset for entity-level sentiment analysis in financial texts by introducing FinEntity, a dataset that annotates financial entity spans and their sentiment in financial news, and they benchmarked pre-trained models like BERT and ChatGPT on this task, achieving competitive results with BERT-based models showing up to 85% accuracy.
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}