Deliberate Multi Parallel Graph Convolution Network for Aspect-Based Sentiment Analysis

Deliberate Multi Parallel Graph Convolution Network for Aspect-Based 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 Deliberate Multi Parallel Graph Convolution Network for Aspect-Based Sentiment Analysis Zelong Su, Bin Gao, Xiaobai Li, Yutong Li, Shutian Liu, Zhengjun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8503855/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task, which needs to detect the sentiment polarity towards a given aspect. Recently, graph neural networks over the dependency tree have been widely applied for aspect-based sentiment analysis. Most existing works generally focus on the extraction of syntactic information, which ignores the impact of different dependencies. In this work, we propose a novel structure named deliberate multi-parallel graph convolutional network (DMP-GCN) that not only extracts the semantic information of the sentence itself but also captures the syntactic information according to different dependencies. To be specific, the proposed DMP-GCN aggregates two blocks, a multi-head attention semantic extraction block (MAS-Block) and a multi-parallel syntactic extraction block (MPS-Block). We construct a set of dependency graphs in MPS-Block to enhance the representations of different dependencies and generate an attention matrix to capture semantic information in MAS-Block. Experimental results on multiple public benchmark datasets illustrate that our proposed model achieves better results compared to other models. Aspect-based sentiment classification Deliberate Multi Parallel GCN MAS-Block Multi-Parallel Syntax Block Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 02 Jan, 2026 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. 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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-8503855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590645475,"identity":"0c32c2e5-223a-4fdf-9721-3f9325a83312","order_by":0,"name":"Zelong Su","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Zelong","middleName":"","lastName":"Su","suffix":""},{"id":590645476,"identity":"dd69a330-5bd9-471f-b6cb-0e79d4164726","order_by":1,"name":"Bin Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYHACNoYEBpsEPoYDYDbRWtIS2EjTwsBwOIENziYE5N0PH3vwcMf5PDbGMwYMH8oOM/DPbsCvxfBMWrpB4pnbxWwMZwwYZ5w7zCBx5wABLQ05ZhKJbbcT24BamHnbDjMYSCQQ0NL/BqTlHETLX2K0yEuAbTkA0cJIjBYDiWdAv7QlA7UcKzjYcy6dR+IGIVv6k489/Nlml9gvcXjjgx9l1nL8MwjZcgDGkjgAjkwe/OpBtjTAWPwNuFWNglEwCkbByAYAkXFFw+ttf5kAAAAASUVORK5CYII=","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Gao","suffix":""},{"id":590645477,"identity":"7104323d-f0c1-45d6-9ab4-612afe360e35","order_by":2,"name":"Xiaobai Li","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobai","middleName":"","lastName":"Li","suffix":""},{"id":590645478,"identity":"2e5e4bd3-ff84-40a1-8186-f03577109912","order_by":3,"name":"Yutong Li","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Li","suffix":""},{"id":590645479,"identity":"31d1b100-2243-4400-a2c4-2be1d666f9b2","order_by":4,"name":"Shutian Liu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shutian","middleName":"","lastName":"Liu","suffix":""},{"id":590645480,"identity":"642dc904-d9ee-4114-9d3b-ccf77d1e74b9","order_by":5,"name":"Zhengjun Liu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhengjun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-03 01:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8503855/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8503855/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102964471,"identity":"73cfd73a-d2f9-452a-b6b9-e0e70210f559","added_by":"auto","created_at":"2026-02-19 04:22:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221712,"visible":true,"origin":"","legend":"","description":"","filename":"DMP0102.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8503855/v1_covered_20f3408b-cde5-41f5-ae6c-2798a6664e1d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deliberate Multi Parallel Graph Convolution Network for Aspect-Based 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":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aspect-based sentiment classification, Deliberate Multi Parallel GCN, MAS-Block, Multi-Parallel Syntax Block","lastPublishedDoi":"10.21203/rs.3.rs-8503855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8503855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task, which needs to detect the sentiment polarity towards a given aspect. 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