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http://hdl.handle.net/10397/106696
Title: | Supervised Cross-Momentum Contrast : aligning representations with prototypical examples to enhance financial sentiment analysis | Authors: | Peng, B Chersoni, E Hsu, YY Qiu, L Huang, CR |
Issue Date: | Jul-2024 | Source: | Knowledge-based systems, 8 July 2024, v. 295, 111683 | Abstract: | Financial sentiment analysis plays a pivotal role in understanding market dynamics and investor sentiment. In this paper, we propose the Supervised Cross-Momentum Contrast (SuCroMoCo) framework, a novel approach for financial sentiment analysis. SuCroMoCo leverages supervised contrastive learning and cross-momentum contrast to align financial text representations with prototypical representations based on sentiment categories. This alignment greatly improves classification performance, addressing the limitations of pre-trained language models (PLMs) in fully grasping the intricate nature of financial text. Through extensive experiments, we demonstrate that SuCroMoCo outperforms existing PLMs-based approaches and Large Language Models (LLMs) on diverse benchmark datasets. | Keywords: | Cross-momentum contrast Financial sentiment analysis Pre-trained language models Supervised contrastive learning |
Publisher: | Elsevier BV | Journal: | Knowledge-based systems | ISSN: | 0950-7051 | EISSN: | 1872-7409 | DOI: | 10.1016/j.knosys.2024.111683 | Research Data: | https://github.com/PengBO-O/SuCroMoCo | Rights: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). The following publication Peng, B., Chersoni, E., Hsu, Y.-y., Qiu, L., & Huang, C.-r. (2024). Supervised Cross-Momentum Contrast: Aligning representations with prototypical examples to enhance financial sentiment analysis. Knowledge-Based Systems, 295, 111683 is available at https://doi.org/10.1016/j.knosys.2024.111683. |
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