CoFFEe-Qwen: A Large Language Model for Chinese Financial Sentiment Analysis Using the Contrastive Learning and Fine-Tuning Paradigm.
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| Title: | CoFFEe-Qwen: A Large Language Model for Chinese Financial Sentiment Analysis Using the Contrastive Learning and Fine-Tuning Paradigm. |
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| Authors: | Feng, Wenfang1 1036784024@qq.com, Yang, Chen2 867320505@qq.com, Zhao, Minrui2 2904104078@qq.com, Xia, Zhiyuan2 xia15094905773@163.com, Wang, Fufu2 wangfufu2001@163.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2526-2539. 14p. |
| Subjects: | Contrastive learning, Spelling errors, Machine learning, Language models, Market sentiment |
| Abstract: | Financial sentiment analysis is increasingly recognized as a pivotal component of stock market research, providing a more accurate and efficient means of quantifying and interpreting market sentiment. However, stock commentaries--one of the most prevalent forms of financial text on social media--are often unstructured and rife with typographical errors and colloquial expressions. To mitigate the lack of publicly available Chinese financial sentiment analysis datasets, we constructed a dedicated expert-annotated corpus and further validated the model's robustness on the large-scale Eastmoney Guba benchmark. To address challenges such as misclassification arising from the non-standard nature of stock commentary, we introduce CoFFEe-Qwen (Contrastive Fine-tuned Financial Embedding for Qwen), a novel framework that integrates supervised contrastive learning with parameter-efficient fine-tuning of large language models (LLMs). This approach fully exploits the transfer learning capabilities of LLMs. Furthermore, the CoFFEe mechanism enables the incorporation of heterogeneous domain data, thereby improving the model's representational capacity for financial texts with limited samples, alleviating the scarcity of social media financial data, and enhancing the overall quality of learned feature representations. In addition, to handle typographical errors and colloquial expressions commonly found in stock commentaries, we design a homophonic character perturbation mechanism that improves the model's robustness and encoding effectiveness when processing noisy input text. Experimental results show that CoFFEe-Qwen consistently outperforms baseline models across multiple metrics--including accuracy, F1-score, precision, and recall--demonstrating its clear superiority in sentiment analysis tasks involving stock commentaries with typographical noise and informal language. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CoFFEe-Qwen: A Large Language Model for Chinese Financial Sentiment Analysis Using the Contrastive Learning and Fine-Tuning Paradigm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Feng%2C+Wenfang%22">Feng, Wenfang</searchLink><relatesTo>1</relatesTo><i> 1036784024@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Chen%22">Yang, Chen</searchLink><relatesTo>2</relatesTo><i> 867320505@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Minrui%22">Zhao, Minrui</searchLink><relatesTo>2</relatesTo><i> 2904104078@qq.com</i><br /><searchLink fieldCode="AR" term="%22Xia%2C+Zhiyuan%22">Xia, Zhiyuan</searchLink><relatesTo>2</relatesTo><i> xia15094905773@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Fufu%22">Wang, Fufu</searchLink><relatesTo>2</relatesTo><i> wangfufu2001@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2526-2539. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Contrastive+learning%22">Contrastive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Spelling+errors%22">Spelling errors</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Market+sentiment%22">Market sentiment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Financial sentiment analysis is increasingly recognized as a pivotal component of stock market research, providing a more accurate and efficient means of quantifying and interpreting market sentiment. However, stock commentaries--one of the most prevalent forms of financial text on social media--are often unstructured and rife with typographical errors and colloquial expressions. To mitigate the lack of publicly available Chinese financial sentiment analysis datasets, we constructed a dedicated expert-annotated corpus and further validated the model's robustness on the large-scale Eastmoney Guba benchmark. To address challenges such as misclassification arising from the non-standard nature of stock commentary, we introduce CoFFEe-Qwen (Contrastive Fine-tuned Financial Embedding for Qwen), a novel framework that integrates supervised contrastive learning with parameter-efficient fine-tuning of large language models (LLMs). This approach fully exploits the transfer learning capabilities of LLMs. Furthermore, the CoFFEe mechanism enables the incorporation of heterogeneous domain data, thereby improving the model's representational capacity for financial texts with limited samples, alleviating the scarcity of social media financial data, and enhancing the overall quality of learned feature representations. In addition, to handle typographical errors and colloquial expressions commonly found in stock commentaries, we design a homophonic character perturbation mechanism that improves the model's robustness and encoding effectiveness when processing noisy input text. Experimental results show that CoFFEe-Qwen consistently outperforms baseline models across multiple metrics--including accuracy, F1-score, precision, and recall--demonstrating its clear superiority in sentiment analysis tasks involving stock commentaries with typographical noise and informal language. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2526 Subjects: – SubjectFull: Contrastive learning Type: general – SubjectFull: Spelling errors Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Language models Type: general – SubjectFull: Market sentiment Type: general Titles: – TitleFull: CoFFEe-Qwen: A Large Language Model for Chinese Financial Sentiment Analysis Using the Contrastive Learning and Fine-Tuning Paradigm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Feng, Wenfang – PersonEntity: Name: NameFull: Yang, Chen – PersonEntity: Name: NameFull: Zhao, Minrui – PersonEntity: Name: NameFull: Xia, Zhiyuan – PersonEntity: Name: NameFull: Wang, Fufu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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