SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916543431966720 |
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| author | Zhu, Senbin He, Chenyuan Liu, Hongde Dong, Pengcheng Zhao, Hanjie Yan, Yuchen Jia, Yuxiang Zan, Hongying Peng, Min |
| author_facet | Zhu, Senbin He, Chenyuan Liu, Hongde Dong, Pengcheng Zhao, Hanjie Yan, Yuchen Jia, Yuxiang Zan, Hongying Peng, Min |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_19140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis Zhu, Senbin He, Chenyuan Liu, Hongde Dong, Pengcheng Zhao, Hanjie Yan, Yuchen Jia, Yuxiang Zan, Hongying Peng, Min Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2412.19140 |