A Heterogeneous Hybrid Re-Sampling (HHR-S) Enhanced Emoji-Aware Generative Expert System for Aspect-Level Tourism Analytics

A Heterogeneous Hybrid Re-Sampling (HHR-S) Enhanced Emoji-Aware Generative Expert System for Aspect-Level Tourism Analytics | 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 A Heterogeneous Hybrid Re-Sampling (HHR-S) Enhanced Emoji-Aware Generative Expert System for Aspect-Level Tourism Analytics Sasipreya Seerach, Wararat Songpan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9029912/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Online tourism reviews are a vital source of experiential intelligence, yet conventional sentiment analysis pipelines face challenges such as severe class imbalance, limited use of affective signals, and unconstrained generative outputs that reduce interpretability and reliability. This study introduces a heterogeneous hybrid re-sampling and emoji-aware generative expert system for aspect-level tourism analytics. The framework incorporates three key mechanisms: (1) structured emoji polarity encoding as an affective knowledge representation layer; (2) imbalance-aware minority synthesis with calibrated decision-boundary optimization to improve classification under skewed data; and (3) taxonomy-guided generative inference for controlled aspect-level intelligence extraction. Unlike previous methods that treat emojis as auxiliary tokens or use large language models without structural constraints, the proposed architecture formally integrates affective and semantic information to enhance robustness and explainability. An experimental evaluation of 13,501 real-world tourism reviews shows that the imbalance-aware configuration achieves 95.94% accuracy and statistically significant improvements in Macro-F1 over text-only and non-resampled baselines. The generative module also demonstrates superior macro-level discrimination over transformer-based aspect-based sentiment analysis baselines. By combining affective computing, imbalance-robust learning, and taxonomy-constrained generative reasoning within a deployable decision-support system, this work advances tourism analytics toward interpretable, stable, and operationally reliable intelligence solutions. Physical sciences/Mathematics and computing Social science/Science technology and society class imbalance learning hybrid re-sampling emoji-aware sentiment analysis generative aspect extraction tourism decision support systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 04 Mar, 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. 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-9029912","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618453912,"identity":"566a01f5-41e1-400c-9ffa-f27e091ead19","order_by":0,"name":"Sasipreya Seerach","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Sasipreya","middleName":"","lastName":"Seerach","suffix":""},{"id":618453914,"identity":"aaf7c996-d33b-4a73-8564-707a262a63c8","order_by":1,"name":"Wararat Songpan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBADOQkeKMuASB0GxjAtEkRrSZxBtBaD42cffq6o+ZM+s+eMAcOPGoY6c4JazqQbS545ZpA7m7fHgLHnGIOEZQMBLWYH0hgkG9gMcufx8xgw8DYAHXaAkJbzz5h/NvwzSJcDamH8S5SWG2lsko1tBgnSQIcxE2WL/Y1nbJaNfcaGM3uOFRyWOSYhuYGQFsn+NOabDd/k5CXOJG98+KbGhp+gLSgAqFiCFPWjYBSMglEwCnABACBfOkZWTnAaAAAAAElFTkSuQmCC","orcid":"","institution":"Khon Kaen University","correspondingAuthor":true,"prefix":"","firstName":"Wararat","middleName":"","lastName":"Songpan","suffix":""}],"badges":[],"createdAt":"2026-03-04 11:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9029912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9029912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106642631,"identity":"cf02516a-f407-4ccc-bdd9-4f4376e00b82","added_by":"auto","created_at":"2026-04-10 18:46:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170487,"visible":true,"origin":"","legend":"","description":"","filename":"0532026HHRSEmojiAware.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9029912/v1_covered_199f2fe6-1ff9-4e18-8d54-6baa9439816f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Heterogeneous Hybrid Re-Sampling (HHR-S) Enhanced Emoji-Aware Generative Expert System for Aspect-Level Tourism Analytics","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"class imbalance learning, hybrid re-sampling, emoji-aware sentiment analysis, generative aspect extraction, tourism decision support systems","lastPublishedDoi":"10.21203/rs.3.rs-9029912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9029912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOnline tourism reviews are a vital source of experiential intelligence, yet conventional sentiment analysis pipelines face challenges such as severe class imbalance, limited use of affective signals, and unconstrained generative outputs that reduce interpretability and reliability. This study introduces a heterogeneous hybrid re-sampling and emoji-aware generative expert system for aspect-level tourism analytics. The framework incorporates three key mechanisms: (1) structured emoji polarity encoding as an affective knowledge representation layer; (2) imbalance-aware minority synthesis with calibrated decision-boundary optimization to improve classification under skewed data; and (3) taxonomy-guided generative inference for controlled aspect-level intelligence extraction. Unlike previous methods that treat emojis as auxiliary tokens or use large language models without structural constraints, the proposed architecture formally integrates affective and semantic information to enhance robustness and explainability. An experimental evaluation of 13,501 real-world tourism reviews shows that the imbalance-aware configuration achieves 95.94% accuracy and statistically significant improvements in Macro-F1 over text-only and non-resampled baselines. The generative module also demonstrates superior macro-level discrimination over transformer-based aspect-based sentiment analysis baselines. 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