@inproceedings{zhu-etal-2025-silc,
title = "{SILC}-{EFSA}: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis",
author = "Zhu, Senbin and
He, ChenYuan and
Liu, Hongde and
Dong, Pengcheng and
Zhao, Hanjie and
Yan, Yuchen and
Jia, Yuxiang and
Zan, Hongying and
Peng, Min",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.333/",
pages = "4980--4992",
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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%0 Conference Proceedings
%T SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
%A Zhu, Senbin
%A He, ChenYuan
%A Liu, Hongde
%A Dong, Pengcheng
%A Zhao, Hanjie
%A Yan, Yuchen
%A Jia, Yuxiang
%A Zan, Hongying
%A Peng, Min
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhu-etal-2025-silc
%X 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.
%U https://aclanthology.org/2025.coling-main.333/
%P 4980-4992
Markdown (Informal)
[SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis](https://aclanthology.org/2025.coling-main.333/) (Zhu et al., COLING 2025)
ACL
- Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, and Min Peng. 2025. SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4980–4992, Abu Dhabi, UAE. Association for Computational Linguistics.