A capsule-based hierarchical graph reasoning model incorporating homologous and heterogeneous information for sentiment analysis

A capsule-based hierarchical graph reasoning model incorporating homologous and heterogeneous information for 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 Article A capsule-based hierarchical graph reasoning model incorporating homologous and heterogeneous information for sentiment analysis Kexin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7289832/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Although cross-modal complementary mechanisms dominate multimodal sentiment analysis, simultaneously acquiring multisource data such as text and audio in practical applications remains challenging. To address this issue, this paper proposes a capsule-based hierarchical graph reasoning model that incorporates homologous and heterogeneous information. This model jointly models the waveform and mel-frequency cepstral coefficients (MFCCs) of audio signals, leveraging their complementarity to enhance sentiment recognition capabilities under single-source data conditions. In the spectral coefficient mode, a region-level sparse reasoning module is introduced to combine local correlations within the spectrum for region-level frequency domain reasoning and feature extraction, thereby perceiving local sentiment features. Subsequently, an emotion capsule graph reasoning module is designed. This module employs a dynamic routing mechanism to map local features into higher-level capsules, capturing emotional representations across various segments of the spectrum. Additionally, it constructs semantic association graphs between higher-level capsules and spectral segments, mining emotional connections between segments to achieve sentiment modeling of entire sentences. In the audio mode, a multi-scale temporal feature reasoning fusion method is proposed. This method utilizes multi-scale convolutional networks to comprehensively extract local features from audio signals and bidirectional LSTM to capture contextual dependencies in the time dimension, thereby enhancing the ability to model emotional evolution. Finally, data from both modes are fused and used for category prediction. Experimental results demonstrate that the proposed method achieves competitive performance on several mainstream datasets. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Emotion recognition Capsule neural network Convolutional neural network Deep learning Attention mecha- nism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Sep, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 29 Aug, 2025 Editor invited by journal 20 Aug, 2025 Submission checks completed at journal 17 Aug, 2025 First submitted to journal 17 Aug, 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. We do this by developing innovative software and high quality services for the global research community. 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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-7289832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511555407,"identity":"40a24dc2-5bbf-4aea-99fe-5ff62de8b8d9","order_by":0,"name":"Kexin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNmaGxMc//0gws/E3HyBOCx97w2NjxgYbdj6JYwnEaZHjOfhMmLEhjV+OIceASIdJJKcxF+44LM3GcObjjTcMdnK6DQS1pKU9nnnmsDEbc+9myzkMycZmBwhqyUk34GE7nMzGcHabNA/DgcRthLXkf5MAaqlvY8h5RqQWngNp0rxtacxsDDlsRGphb0g2nHHGhplN4pix5RwDIvwi38yQ+OBDhQSzfH/zwxtvKuzkCGpBARI8REYNshZSdYyCUTAKRsGIAAB7xT7sOOXLFQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":true,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-08-04 10:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7289832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7289832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90861667,"identity":"0557ae61-d404-4d66-a005-214d49fa2cba","added_by":"auto","created_at":"2025-09-09 06:18:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704017,"visible":true,"origin":"","legend":"","description":"","filename":"ACapsulebasedHierarchicalGraphReasoningModelIncorporatingHomologousandHeterogeneousInformationforSentimentAnalysis20250817.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7289832/v1_covered_2041b6c5-2adc-4c89-97da-8804c73523a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A capsule-based hierarchical graph reasoning model incorporating homologous and heterogeneous information for sentiment analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emotion recognition, Capsule neural network, Convolutional neural network, Deep learning, Attention mecha- nism","lastPublishedDoi":"10.21203/rs.3.rs-7289832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7289832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Although cross-modal complementary mechanisms dominate multimodal sentiment analysis, simultaneously acquiring multisource data such as text and audio in practical applications remains challenging. 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