Disentangled Representation Learning with Temporal Smoothness Constraints for Multimodal Sentiment Analysis

Disentangled Representation Learning with Temporal Smoothness Constraints for Multimodal Sentiment Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Disentangled Representation Learning with Temporal Smoothness Constraints for Multimodal Sentiment Analysis Yihao Xu, Hai Huan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6419812/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Multimedia Systems → Version 1 posted 12 You are reading this latest preprint version Abstract The goal of multimodal sentiment analysis is to efficiently identify and interpret human emotions by integrating multiple modalities (e.g., text, audio, and video). Traditional representation learning techniques often fail to adequately address inter-modal heterogeneity and temporal continuity, particularly as multimodal sentiment analysis tasks grow in complexity. Consequently, these methods struggle to achieve effective cross-modal fusion while mitigating redundant information and noise interference. To address these challenges, we propose DRTSC, a novel multimodal sentiment analysis framework. First, the framework employs disentangled representation learning to extract shared and private features; introduces a temporal smoothness loss to enforce consistency in audio and video features; and incorporates adversarial loss with backward tuning. Second, a textual hierarchical guidance module coordinates audio and video emotional expressions by leveraging affective cues from text. Finally, efficient feature fusion is achieved through cross-modal interaction layers. Extensive experiments on CMU-MOSI and CMU-MOSEI benchmarks demonstrate that the proposed model achieves state-of-the-art performance in sentiment analysis tasks. multimodal sentiment analysis disentangled representation learning temporal smoothness loss textual hierarchical guidance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Multimedia Systems → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 11 May, 2025 Editor assigned by journal 05 May, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6419812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456070945,"identity":"47db7999-22ff-47ac-b708-5e0beed1c9da","order_by":0,"name":"Yihao Xu","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yihao","middleName":"","lastName":"Xu","suffix":""},{"id":456070946,"identity":"33a592d8-dae8-4b7e-8040-6164abc60579","order_by":1,"name":"Hai Huan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3PIQvCQBTA8TcOZplcfQvqVziwCH6ZDYPFPBQME2FJtE4U/RbLNx5okVkVDFqsmsQieGIyyJ3NcH84eA/eLxyAzfaHuaXh+o5dNbxXA8K9VYCNzeuYGRI/7QjsJa/RlAgZCLFfHMIx30q4RgR8FuuIDMJpdg4TbIGTFgR4kBqSx5L8jBRhwMoJgcBAQ8gZDB9zRTgBe5gQP2EM/FgRaAFzTAj3XBdwRXX1F5GPiraHOw1xa5cbYJ8qy0l+Ot6jZpWnGvKRVM/74d5ms9ls33oCn5pDJ8QT2eMAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hai","middleName":"","lastName":"Huan","suffix":""}],"badges":[],"createdAt":"2025-04-10 11:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6419812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6419812/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00530-025-02138-0","type":"published","date":"2026-02-03T15:59:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102234272,"identity":"ea195484-465f-49ea-b09b-34aa3ea5f0f8","added_by":"auto","created_at":"2026-02-09 16:08:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1335026,"visible":true,"origin":"","legend":"","description":"","filename":"DRTSC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6419812/v1_covered_33c8465b-5005-4c5b-b1fe-1c60ddec681c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disentangled Representation Learning with Temporal Smoothness Constraints for Multimodal Sentiment Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"multimodal sentiment analysis, disentangled representation learning, temporal smoothness loss, textual hierarchical guidance","lastPublishedDoi":"10.21203/rs.3.rs-6419812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6419812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe goal of multimodal sentiment analysis is to efficiently identify and interpret human emotions by integrating multiple modalities (e.g., text, audio, and video). 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