EFSA: Towards Event-Level Financial Sentiment Analysis

TL;DR

This paper introduces EFSA, a novel event-level financial sentiment analysis framework that extracts detailed event information from financial texts using a large Chinese dataset and a Chain-of-Thought LLM approach, achieving state-of-the-art results.

Contribution

It redefines financial sentiment analysis at the event level with a new dataset, task formulation, and a Chain-of-Thought LLM-based method for improved accuracy.

Findings

Proposed a new event-level sentiment analysis task with quintuples.

Created a large-scale Chinese financial news dataset with over 12,000 articles.

Achieved state-of-the-art benchmarking scores with the proposed approach.

Abstract

In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and fine-grained event categories. Under this setting, we formulate the \textbf{E}vent-Level \textbf{F}inancial \textbf{S}entiment \textbf{A}nalysis~(\textbf{EFSA} for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing $12,160$ news articles and $13,725$ quintuples is publicized as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://anonymous.4open.science/r/EFSA-645E