SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
This addresses the problem of fine-grained sentiment analysis in finance for researchers and practitioners, though it appears incremental as it builds on existing large language model and GNN techniques.
The authors tackled the scarcity of entity-level datasets for financial sentiment analysis by constructing the largest English and Chinese datasets to date, and proposed a two-stage method (SILC) that achieved state-of-the-art performance on these new datasets.
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.