Comparative evaluation of Transformer-Based (RoBERTa) and Classical (CNN/SBERT) models for early detection of mental disorders from textual data: A Systematic Review

Comparative evaluation of Transformer-Based (RoBERTa) and Classical (CNN/SBERT) models for early detection of mental disorders from textual data: A Systematic Review | 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 Systematic Review Comparative evaluation of Transformer-Based (RoBERTa) and Classical (CNN/SBERT) models for early detection of mental disorders from textual data: A Systematic Review Nadi Maroua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9482601/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 mental health disorders continue to rise globally and access to specialised care remains a persistent challenge, AI-powered conversational agents have emerged as a credible avenue for early screening. This systematic review, conducted in ac cordance with PRISMA guidelines and drawing on databases including PubMed, Springer, and IEEE Xplore, critically assesses the diagnostic validity and clinical e ectiveness of a range of NLP models , from classical architectures such as CNN and SBERT-CNN to Transformer-based models, most notably RoBERTa. Studies reporting standardised clinical performance metrics and user acceptability data were retained; those relying on insu cient sample sizes or purely static applications were excluded. Findings indicate that classical models are constrained in their semantic modelling capacity, whereas Transformer-based architectures, RoBERTa in particu lar, demonstrate richer contextual representation, enabling more nuanced detection of subtle linguistic cues. Despite superior performance, RoBERTa still poses notable challenges: high computational cost, dependence on annotated training corpora, and susceptibility to linguistic bias. In conclusion, RoBERTa stands out as the most ef fective model for early mental disorder screening; however, its clinical integration calls for lighter architectures, improved interpretability, and robust ethical frame works grounded in meaningful human oversight. Artificial Intelligence mental health early detection mental disorders Systematic Review Machine Learning conversational agent ethics NLP 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-9482601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":626944674,"identity":"8ee4387d-addb-4837-80e4-bc6d9cacc0a2","order_by":0,"name":"Nadi Maroua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACgwNgir2BjVQtPAdI0AKhJBKI1GLOfnjjAYaKO/b8M9+YPS5gsJNnEDv8AK8Wy560ggMMZ54lzridY248gyHZsEE6zQC/qw7kGBxgbDucwHA7x0yah4E5gUE6gYCW82+AWv4dtpe/eQakpR6oJf0Dfi03QLY0HGbccIMHpAVonXQOAVtuPCs4kHDscOLGM2ll0jMMjhu2SecUEHBY8uYPH2oO28sdP7xNuqCiWp5fOn0DXi1gkAClmUFxRHyEQrSMglEwCkbBKMACAAseRAi4iCtpAAAAAElFTkSuQmCC","orcid":"","institution":"National School of Applied Sciences - BERRECHID","correspondingAuthor":true,"prefix":"","firstName":"Nadi","middleName":"","lastName":"Maroua","suffix":""}],"badges":[],"createdAt":"2026-04-21 10:15:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9482601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9482601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108180983,"identity":"a17507b8-b978-4c3d-a8be-d200d9538c5c","added_by":"auto","created_at":"2026-04-30 08:55:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":334857,"visible":true,"origin":"","legend":"","description":"","filename":"ComparativeevaluationofTransformerBasedandClassicalmodelsforearlydetectionofmentaldisordersAsystematicreview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9482601/v1_covered_ebb8adc4-b3c2-4d07-b123-b3129c609779.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative evaluation of Transformer-Based (RoBERTa) and Classical (CNN/SBERT) models for early detection of mental disorders from textual data: A Systematic Review\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National school of applied sciences - berrechid","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, mental health, early detection, mental disorders, Systematic Review, Machine Learning, conversational agent, ethics, NLP","lastPublishedDoi":"10.21203/rs.3.rs-9482601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9482601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs mental health disorders continue to rise globally and access to specialised care remains a persistent challenge, AI-powered conversational agents have emerged as a credible avenue for early screening. This systematic review, conducted in ac cordance with PRISMA guidelines and drawing on databases including PubMed, Springer, and IEEE Xplore, critically assesses the diagnostic validity and clinical e ectiveness of a range of NLP models , from classical architectures such as CNN and SBERT-CNN to Transformer-based models, most notably RoBERTa. Studies reporting standardised clinical performance metrics and user acceptability data were retained; those relying on insu cient sample sizes or purely static applications were excluded. Findings indicate that classical models are constrained in their semantic modelling capacity, whereas Transformer-based architectures, RoBERTa in particu lar, demonstrate richer contextual representation, enabling more nuanced detection of subtle linguistic cues. Despite superior performance, RoBERTa still poses notable challenges: high computational cost, dependence on annotated training corpora, and susceptibility to linguistic bias. In conclusion, RoBERTa stands out as the most ef fective model for early mental disorder screening; however, its clinical integration calls for lighter architectures, improved interpretability, and robust ethical frame works grounded in meaningful human oversight.\u003c/p\u003e","manuscriptTitle":"Comparative evaluation of Transformer-Based (RoBERTa) and Classical (CNN/SBERT) models for early detection of mental disorders from textual data: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 10:36:52","doi":"10.21203/rs.3.rs-9482601/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2e6d8af-7e59-4433-9c33-662881c6cdc2","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T10:36:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 10:36:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9482601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9482601","identity":"rs-9482601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}