Identifying Customer Priorities in Online Reviews through Sequence-to-Sequence Learning with Dual Contextual Attention

Identifying Customer Priorities in Online Reviews through Sequence-to-Sequence Learning with Dual Contextual Attention | 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 Identifying Customer Priorities in Online Reviews through Sequence-to-Sequence Learning with Dual Contextual Attention Sam Rahimzadeh Holagh, Jinfeng Zhou, Bugao Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8340383/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 User reviews on e-commerce platforms offer real-time feedback on customer experiences. However, the large volume and unstructured nature of reviews make it challenging for businesses to identify the key aspects that matter most to consumers. This study introduces a sequence-to-sequence learning model that integrates global positional attention with local syntactic attention to extract aspect terms capturing the product and service attributes emphasized in reviews. A multi-stage aspect summarization process, combining dimensionality reduction, semantic clustering, and LLM based refinement, is utilized to distill hundreds of extracted terms into a concise set of interpretable primal words highlighting customer major concerns. Experiments on three annotated SemEval 2014 datasets demonstrate superior extraction accuracy compared to eight established baselines. Application to five large Amazon review datasets across different domains further shows strong generalizability in identifying coherent primal terms for each dataset, providing actionable themes aligned with consumer priorities. sequence-to-sequence learning dependency parsing aspect extraction aspect summarization Full Text Additional Declarations The authors declare no competing 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. 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-8340383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559075724,"identity":"d4febbf9-254d-4cfe-8e9c-d5bc84e03aca","order_by":0,"name":"Sam Rahimzadeh Holagh","email":"","orcid":"","institution":"University of North Texas","correspondingAuthor":false,"prefix":"","firstName":"Sam","middleName":"Rahimzadeh","lastName":"Holagh","suffix":""},{"id":559075725,"identity":"5ed9ad1a-e5e0-41f3-ac50-fc0ed2ad9f12","order_by":1,"name":"Jinfeng Zhou","email":"","orcid":"","institution":"University of North 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