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    <abstract>Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. This RAG-based augmentation significantly modernizes the data’s alignment with current financial language. Furthermore, a robust BERT-BiGRU judge model verifies that the sentiment of the original annotations is faithfully preserved, ensuring the generation of high-quality, temporally relevant, and sentiment-consistent data suitable for advancing FSA model development.</abstract>
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%0 Conference Proceedings
%T Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation
%A Kumar, Rohit
%A Nolbaria, Chandan
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F kumar-nolbaria-2025-bridging
%X Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. This RAG-based augmentation significantly modernizes the data’s alignment with current financial language. Furthermore, a robust BERT-BiGRU judge model verifies that the sentiment of the original annotations is faithfully preserved, ensuring the generation of high-quality, temporally relevant, and sentiment-consistent data suitable for advancing FSA model development.
%R 10.18653/v1/2025.acl-srw.98
%U https://aclanthology.org/2025.acl-srw.98/
%U https://doi.org/10.18653/v1/2025.acl-srw.98
%P 1246-1254

Markdown (Informal)

[Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation](https://aclanthology.org/2025.acl-srw.98/) (Kumar & Nolbaria, ACL 2025)

• Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation (Kumar & Nolbaria, ACL 2025)

ACL

• Rohit Kumar and Chandan Nolbaria. 2025. Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 1246–1254, Vienna, Austria. Association for Computational Linguistics.