FinEntity: Entity-level Sentiment Classification for Financial Texts

• URL: http://arxiv.org/abs/2310.12406v1
• Date: Thu, 19 Oct 2023 01:38:40 GMT
• Title: FinEntity: Entity-level Sentiment Classification for Financial Texts
• Authors: Yixuan Tang, Yi Yang, Allen H Huang, Andy Tam, Justin Z Tang
• Abstract summary: In the financial domain, conducting entity-level sentiment analysis is crucial for accurately assessing the sentiment directed toward a specific financial entity. We introduce an entity-level sentiment classification dataset, called textbfFinEntity, that annotates financial entity spans and their sentiment in financial news.
• Score: 15.467477195487763
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \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}

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