SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
Abstract
In recent years, fine-grained sentiment analysis in finance has gained significant attention, but the scarcity of entity-level datasets remains a key challenge. To address this, we have constructed the largest English and Chinese financial entity-level sentiment analysis datasets to date. Building on this foundation, we propose a novel two-stage sentiment analysis approach called Self-aware In-context Learning Correction (SILC). The first stage involves fine-tuning a base large language model to generate pseudo-labeled data specific to our task. In the second stage, we train a correction model using a GNN-based example retriever, which is informed by the pseudo-labeled data. This two-stage strategy has allowed us to achieve state-of-the-art performance on the newly constructed datasets, advancing the field of financial sentiment analysis. In a case study, we demonstrate the enhanced practical utility of our data and methods in monitoring the cryptocurrency market. Our datasets and code are available at https://github.com/NLP-Bin/SILC-EFSA.
Keywords
Cite
@article{arxiv.2412.19140,
title = {SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis},
author = {Senbin Zhu and Chenyuan He and Hongde Liu and Pengcheng Dong and Hanjie Zhao and Yuchen Yan and Yuxiang Jia and Hongying Zan and Min Peng},
journal= {arXiv preprint arXiv:2412.19140},
year = {2024}
}
Comments
This paper is to be published in the Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025)