MRDAGT: A Multi-Relational Dual-Attention GraphTransformer for Fine-Grained Sentiment Analysis

MRDAGT: A Multi-Relational Dual-Attention GraphTransformer for Fine-Grained 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 MRDAGT: A Multi-Relational Dual-Attention GraphTransformer for Fine-Grained Sentiment Analysis Anusha P. Anilkumar, Sookyun Kim, Yeo-Chan Yoon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6337155/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of specific aspects within a text, requiring adeep understanding of syntactic, semantic, and discourse-level dependencies. While existing graph-based models leveragedependency structures and relational attention mechanisms, they often overlook the interplay between multiple relationtypes and the importance of attention regularization for interpretability. In this work, we introduce the Multi-Relational DualAttention Graph Transformer (MRDAGT), which integrates syntactic, semantic, and discourse relations into a unified graphstructure. The model employs a dual-attention mechanism, where one head captures local token-level interactions, andthe other focuses on aspect-oriented attention, ensuring that sentiment-relevant context is preserved. Additionally, attentionregularization constraints, including an entropy-based penalty and an L1 sparsity term, enhance interpretability by refiningattention distributions. The proposed method achieves state-of-the-art (SOTA) performance on multiple benchmark datasets,demonstrating its effectiveness and generalizability. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology ABSA multi-relational graph dual-attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Editor invited by journal 16 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 30 Mar, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6337155","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449462908,"identity":"98b0aa2d-880a-48ec-9df2-1481f251f95b","order_by":0,"name":"Anusha P. 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