Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models
This provides a valuable resource for advancing real-world financial sentiment analysis tasks, though it is incremental as it focuses on dataset creation rather than new methods.
The authors tackled the lack of high-quality annotated corpora for financial sentiment analysis by creating FinChina SA, a novel Chinese fine-grained dataset for enterprise early warning, and evaluated existing open-source LLMs on it.
Entity-level fine-grained sentiment analysis in the financial domain is a crucial subtask of sentiment analysis and currently faces numerous challenges. The primary challenge stems from the lack of high-quality and large-scale annotated corpora specifically designed for financial text sentiment analysis, which in turn limits the availability of data necessary for developing effective text processing techniques. Recent advancements in large language models (LLMs) have yielded remarkable performance in natural language processing tasks, primarily centered around language pattern matching. In this paper, we propose a novel and extensive Chinese fine-grained financial sentiment analysis dataset, FinChina SA, for enterprise early warning. We thoroughly evaluate and experiment with well-known existing open-source LLMs using our dataset. We firmly believe that our dataset will serve as a valuable resource to advance the exploration of real-world financial sentiment analysis tasks, which should be the focus of future research. The FinChina SA dataset is publicly available at https://github.com/YerayL/FinChina-SA