MyGovEmotion-GNN: A Graph Neural Network Framework for Emotion Recognition on MyGov Comments

MyGovEmotion-GNN: A Graph Neural Network Framework for Emotion Recognition on MyGov Comments | 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 MyGovEmotion-GNN: A Graph Neural Network Framework for Emotion Recognition on MyGov Comments Sushma Yadav, Rudresh Dwivedi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7692781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Natural language processing (NLP), Emotion identification from text is an essential task, with a wide range of applications from sentiment analysis to customer feedback systems, government schemes monitoring, and mental health monitoring. In this study, we introduce MyGovEmotion-GNN, a novel model for textual emotion classification on MyGov comments. Our Approach that uses Graph Neural Networks (GNN) to bridge the gap between traditional text representation techniques and graph-based learning. In contrast to conventional models that only use transformer-based architectures or sequential encoding, our approach makes use of both structural context and semantic richness to predict emotions more reliably. Using cosine similarity of Term Frequency-Inverse Document Frequency (TF-IDF) vectors, our methodology involves modeling text documents as graphs, with nodes representing documents and edges expressing crucial connections. To recognize emotions in comments, we have employed Graph Convolutional Networks (GCN) under GNN based framework to categorize reviews. In our work, we have utilized comments dataset collected from MyGov political website incorporating government schemes and publicly available GoEmotions dataset. Also, we have performed experimental evaluation using publicly available benchmark dataset such as GoEmotions. Further, the experimental results illustrate that the proposed GNN model achieved the better results, attaining 0.67 accuracy score and F1 score of 0.66. MyGov Citizen Engagement NLP User reviews Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7692781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528303883,"identity":"6763ca8a-e122-43ff-918e-f2965cac27b1","order_by":0,"name":"Sushma Yadav","email":"data:image/png;base64,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","orcid":"","institution":"Netaji Subhas University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Sushma","middleName":"","lastName":"Yadav","suffix":""},{"id":528303884,"identity":"fc9f2945-a283-4349-afdd-70e1f5a3dd95","order_by":1,"name":"Rudresh Dwivedi","email":"","orcid":"","institution":"Netaji Subhas University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rudresh","middleName":"","lastName":"Dwivedi","suffix":""}],"badges":[],"createdAt":"2025-09-23 09:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7692781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7692781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93698906,"identity":"12f09a1b-430b-4439-b7ac-7d415a90689d","added_by":"auto","created_at":"2025-10-16 15:09:19","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4200,"visible":true,"origin":"","legend":"","description":"","filename":"7a832420a41b45a981c5c25ee5d485da.json","url":"https://assets-eu.researchsquare.com/files/rs-7692781/v1/3a19dca745a79d841e18825b.json"},{"id":102747467,"identity":"56c0ed49-6012-437a-a35d-98194a45142e","added_by":"auto","created_at":"2026-02-16 09:04:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1420072,"visible":true,"origin":"","legend":"","description":"","filename":"sushmaemotiondetection23Sep.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7692781/v1_covered_a58e4863-86c8-493a-a2d3-ebde6a1c5259.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MyGovEmotion-GNN: A Graph Neural Network Framework for Emotion Recognition on MyGov Comments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MyGov, Citizen Engagement, NLP, User reviews","lastPublishedDoi":"10.21203/rs.3.rs-7692781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7692781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In Natural language processing (NLP), Emotion identification from text is an essential task, with a wide range of applications from sentiment analysis to customer feedback systems, government schemes monitoring, and mental health monitoring. 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