Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106696
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Chinese and Bilingual Studies | en_US |
dc.creator | Peng, B | en_US |
dc.creator | Chersoni, E | en_US |
dc.creator | Hsu, YY | en_US |
dc.creator | Qiu, L | en_US |
dc.creator | Huang, CR | en_US |
dc.date.accessioned | 2024-06-03T02:11:34Z | - |
dc.date.available | 2024-06-03T02:11:34Z | - |
dc.identifier.issn | 0950-7051 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106696 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.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/). | en_US |
dc.rights | 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. | en_US |
dc.subject | Cross-momentum contrast | en_US |
dc.subject | Financial sentiment analysis | en_US |
dc.subject | Pre-trained language models | en_US |
dc.subject | Supervised contrastive learning | en_US |
dc.title | Supervised Cross-Momentum Contrast : aligning representations with prototypical examples to enhance financial sentiment analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 295 | en_US |
dc.identifier.doi | 10.1016/j.knosys.2024.111683 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Knowledge-based systems, 8 July 2024, v. 295, 111683 | en_US |
dcterms.isPartOf | Knowledge-based systems | en_US |
dcterms.issued | 2024-07 | - |
dc.identifier.scopus | 2-s2.0-85191861161 | - |
dc.identifier.eissn | 1872-7409 | en_US |
dc.identifier.artn | 111683 | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a2727b | - |
dc.identifier.SubFormID | 48141 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Faculty of Humanities | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
dc.relation.rdata | https://github.com/PengBO-O/SuCroMoCo | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S0950705124003186-main.pdf | 1.83 MB | Adobe PDF | View/Open |
Page views
3
Citations as of Jun 30, 2024
Downloads
4
Citations as of Jun 30, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.