mlsanityData to 14 Oct 2026

Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models

arXiv 2306.14096 ↗First posted 25 Jun 2023cs.CL cs.AIcited by 4

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Abstract (arXiv)

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

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 50 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

29 of 50 references.

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Do We Need Domain-Specific Embedding Models? An Empirical Investigationcited by 12
  2. 2025Large Language Models for Subjective Language Understanding: A Surveycited by 9
  3. 2025On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojiscited by 3
  4. 2025Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual datacited by 1