FinEntity: Entity-level Sentiment Classification for Financial Texts

Yixuan Tang, Yi Yang, Allen H Huang, Andy Tam, Justin Z. Tang

Submission Type: Regular Short Paper

Submission Track: Resources and Evaluation

Keywords: Named Entity Recognition, Sentiment Analysis, Financial NLP

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.

Submission Number: 1581