HGTBF-OM: A Hybrid Graph-Based Transformer Framework for Enhanced Opinion Mining in Textual Data

HGTBF-OM: A Hybrid Graph-Based Transformer Framework for Enhanced Opinion Mining in Textual Data | 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 HGTBF-OM: A Hybrid Graph-Based Transformer Framework for Enhanced Opinion Mining in Textual Data Madhurika B, Naga Malleswari D This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6311996/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 As one such process, opinion mining carries immense significance in deriving actionable insights from textual information, facilitating data-driven decisions across sectors. While existing methodologies have attained great strides, including hybrid deep learning models and artificial neural networks, they need help addressing deep sentiments and contextual relationships in complex datasets. The current state-of-the-art approaches either is reliant on heuristics-based methods such as LSTMs or independent graph learning techniques or less sophisticated strategies based on either one of graph-based techniques or domain transformers without ensembling their respective characteristics, thus causing them to lose the power of harmony between the two as a pioneering approach. To solve these challenges, this study presents the Hybrid Graph Transformer-Based Framework for Opinion Mining (HGTBF-OM). It integrates graph representation, TransformerConv layers, and multi-head attention mechanisms. Experimental validation on the Zomato dataset demonstrated that the proposed framework outperformed current models, achieving a commendable accuracy of 99.01%, surpassing TransLSTM, So-haTRed, and Hybrid GCN-RF. The focus on these results demonstrates the framework’s facet-wise analysis capacity and its performance on balanced data. It is helpful for e-commerce, healthcare, and social media monitoring applications where the precision of sentiment analysis is vital. This framework addresses the limitations of classical methods and provides a scalable, efficient approach to contemporary opinion-related sentiment analysis tasks. Future work plans to broaden its use on multi-lingual datasets, improve computational efficiency, and assess its usability in dynamic and task-specific sentiment analysis scenarios. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Software Physical sciences/Engineering Opinion Mining Hybrid Graph Transformer Sentiment Analysis Deep Learning Framework Textual Data Analysis Full Text Additional Declarations Competing interest reported. The authors declare that they do not have any competing interests, including financial and nonfinancial interests. 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-6311996","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488974611,"identity":"e6f4cdd5-89ea-4096-87e8-ac2deada0e87","order_by":0,"name":"Madhurika B","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIiWNgGAWjYBACAzBiYAOxzYBYwoCBvQEkbkGKFp4DIHEJQloYYFqAPIkEEI1bi7lE8sbPFTV8iWvbD297zLvHwph/5vOrG34USDDwt3cnYNNiOSOtWPLMMbbEbWfSyo15nkmYSdzOKbvZA3SYxJmzG7A67EaOgWQDG1DLgRwzaZ4DEjYMt3PSbvAAtRhI5OLSYvyz4R9Qy/k3EC3yN8+k3fyDX4uZZGMbUMsNiC1mBjfYj93GZ4tlz7Myy8Y+NuNtN56VSc45IGFseCaH7baMgQQPLr+Ysydvvtnw7ZjstvPJ2yTeHKgznHf8+LObb/7YyPG392LVAgXHkDk84JjiwaMcBGqQOewPCKgeBaNgFIyCEQYAwIJl4X58vwkAAAAASUVORK5CYII=","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":true,"prefix":"","firstName":"Madhurika","middleName":"","lastName":"B","suffix":""},{"id":488974612,"identity":"b18bd706-3073-4a0f-b568-3aa2ab4ac566","order_by":1,"name":"Naga Malleswari D","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Naga","middleName":"Malleswari","lastName":"D","suffix":""}],"badges":[],"createdAt":"2025-03-26 11:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6311996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6311996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89566880,"identity":"65d4c0fb-23f9-4324-80a5-aa7659a4a94b","added_by":"auto","created_at":"2025-08-21 11:08:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptMahurika1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6311996/v1_covered_e7fdc6db-a57f-44a5-9e9c-992fce0d36f6.pdf"}],"financialInterests":"Competing interest reported. 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