Benchmarking Multilingual Sentiment Analysis Models for the Hospitality Industry: A Case Study of Hotel Reviews in Vietnam

Benchmarking Multilingual Sentiment Analysis Models for the Hospitality Industry: A Case Study of Hotel Reviews in Vietnam | 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 Benchmarking Multilingual Sentiment Analysis Models for the Hospitality Industry: A Case Study of Hotel Reviews in Vietnam Quoc Lap Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8680561/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract The explosive growth of online tourism platforms has generated massive multilingual hotel reviews, creating both opportunities and challenges for hospitality businesses in emerging markets. Vietnamese hotels receive feedback in multiple languages, making manual sentiment analysis impractical. This study benchmarks five state-of-the-art Transformer models (XLM-RoBERTa, mBERT, mDeBERTa, DistilBERT, multilingual-E5) for automated sentiment classification using 59,377 authentic hotel reviews spanning 14 languages and five sentiment categories with highly imbalanced distributions reflecting realistic business scenarios. Results reveal that multilingual-E5 achieves the highest overall performance (82% accuracy, macro F1=0.62) with superior minority class handling, while DistilBERT provides comparable accuracy (80%) with significantly reduced computational requirements. Critically, XLM-RoBERTa exhibits catastrophic failure on minority classes despite strong benchmark performance (77% accuracy, 0.03 recall on negative reviews), demonstrating that standard NLP benchmarks do not predict domain-specific effectiveness. We provide evidence-based model selection guidelines linking business characteristics to appropriate choices, quantitative cost-benefit analysis demonstrating 710% ROI for typical deployments, and actionable implementation strategies. These findings enable small and medium hospitality enterprises to adopt AI-powered sentiment analysis sustainably, supporting UN Sustainable Development Goals 8, 9, and 12 by democratizing access to sophisticated NLP capabilities while promoting responsible computational practices. Sentiment analysis Multilingual NLP Hospitality industry Transformer models Hotel reviews Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Apr, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor invited by journal 04 Mar, 2026 Editor assigned by journal 24 Jan, 2026 Submission checks completed at journal 24 Jan, 2026 First submitted to journal 23 Jan, 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. 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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-8680561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601924953,"identity":"118a80eb-f11d-4588-9e75-4ddc03798b56","order_by":0,"name":"Quoc Lap Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie2OsWrDQAyGVQTycuRWlUDyChcMgZI8jEPAWdwsXby1YPBW+gJ+jswuB56cvdClJatbDIGObRWTdsq5GTvch06In/uQADyef8ig6+kcSWflb8J9CnW9joMBVxGUx+Rv5SK3egSJOVMJttVLQzgkqPf7FuyIAB+fFdi1U1Hr1aRQFBLeb7gEGxLQcibKjfuwZDpUrJYE240cZhc5KEkgXty5FN3IB8O3OSS7VhQZ9Ee/woctkUFZB4fDIgJFosx7lPf4sigjJK6mXJvVJEcKrwrjVrS+rvjt8wvHD9muTdPZWAfZ61OTslMBUF0dMfLwZzhP8Xg8Hs8JvgEoCEeZuQ5A8gAAAABJRU5ErkJggg==","orcid":"","institution":"Vietnam National University, Ho Chi Minh City","correspondingAuthor":true,"prefix":"","firstName":"Quoc","middleName":"Lap","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2026-01-23 15:10:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8680561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8680561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104201020,"identity":"8cfba04a-8c52-4b5c-8b75-7ffb99bb2b01","added_by":"auto","created_at":"2026-03-09 05:33:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":302139,"visible":true,"origin":"","legend":"","description":"","filename":"MultilingualsentimentSN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8680561/v1_covered_19241c1a-3a36-4b89-8fa2-fd2e3308bba4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benchmarking Multilingual Sentiment Analysis Models for the Hospitality Industry: A Case Study of Hotel Reviews in Vietnam","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":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Sentiment analysis, Multilingual NLP, Hospitality industry, Transformer models, Hotel reviews","lastPublishedDoi":"10.21203/rs.3.rs-8680561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8680561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The explosive growth of online tourism platforms has generated massive multilingual hotel reviews, creating both opportunities and challenges for hospitality businesses in emerging markets. Vietnamese hotels receive feedback in multiple languages, making manual sentiment analysis impractical. This study benchmarks five state-of-the-art Transformer models (XLM-RoBERTa, mBERT, mDeBERTa, DistilBERT, multilingual-E5) for automated sentiment classification using 59,377 authentic hotel reviews spanning 14 languages and five sentiment categories with highly imbalanced distributions reflecting realistic business scenarios. Results reveal that multilingual-E5 achieves the highest overall performance (82% accuracy, macro F1=0.62) with superior minority class handling, while DistilBERT provides comparable accuracy (80%) with significantly reduced computational requirements. Critically, XLM-RoBERTa exhibits catastrophic failure on minority classes despite strong benchmark performance (77% accuracy, 0.03 recall on negative reviews), demonstrating that standard NLP benchmarks do not predict domain-specific effectiveness. We provide evidence-based model selection guidelines linking business characteristics to appropriate choices, quantitative cost-benefit analysis demonstrating 710% ROI for typical deployments, and actionable implementation strategies. 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