SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis

• URL: http://arxiv.org/abs/2412.19140v1
• Date: Thu, 26 Dec 2024 09:53:01 GMT
• Title: SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
• Authors: Senbin Zhu, Chenyuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng,
• Abstract summary: We propose a novel two-stage sentiment analysis approach called Self-aware In-aware 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.
• Score: 9.48177379759274
• License:
• 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.

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