FinEntity: Entity-level Sentiment Classification for Financial Texts

Abstract

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 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. * The views and analysis expressed in this paper are those of the authors and do not necessarily represent the views of HKMA. All errors are the authors' own.1 https://www.wsj.com/articles/microsoft-and-googlewill-both-have-to-bear-ais-costs-11674006102