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The work proposes an AI framework that combines NLP with privacy-protective mechanisms to recognize emotions from users’ text. It formulates emotion recognition as multi-label classification and fine-tunes transformer models including BERT-base-uncased and ModernBERT Large. User data is secured using AES-256 encryption plus Supabase authentication and role-based access control. The study reports rapid convergence and improved sensitivity, especially for sarcasm and mixed emotional states.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ai-powered-mental-health-application-with-data-privacy-preservation-article/451111/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ai-powered-mental-health-application-with-data-privacy-preservation-article/451111.png","ImageObject",300,407,{"name":92,"@type":93},"Nguyễn Văn Học","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the proposed AI framework address?","Question",{"text":112,"@type":113},"It addresses the increasing need for digital mental-health support by recognizing emotions expressed in users’ text and providing early intervention support.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the framework preserve user privacy?",{"text":117,"@type":113},"It uses AES-256 encryption, Supabase authentication, and role-based access control to prevent unauthorized access and maintain confidentiality.",{"name":119,"@type":110,"acceptedAnswer":120},"Which transformer models are used and how are they compared?",{"text":121,"@type":113},"The paper fine-tunes and compares BERT-base-uncased and ModernBERT Large for emotion detection, evaluating their suitability for mental-health scenarios.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},451111,1791289849,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41,"preview_page_count":4},1374402739827,"https://ap-avatar.wpscdn.com/avatar/14000c97e7351f1a627?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787885694763230660","MethodsX 16 (2026) 103756  \nContents lists available at ScienceDirect  \nMethodsX  \njournal [homepage:](homepage: www.elsevier.com/locate/methodsx)[ www.elsevier.com/locate/methodsx](homepage: www.elsevier.com/locate/methodsx)  \n| AI-powered mental health application with data privacy preservation | |\n| --- | --- |\n| Pooja Baganea,* , Anushree Dahiyaa, Shardul Kacheriaa, Ansh Sehgala, Shrishti Bajpaia, Obsa Amenu Jebessab\u003Cbr>a Symbiosis Institute of Technology –Pune Campus, Symbiosis International (Deemed University), Pune, India b Jimma Institute of Technology, Jimma, Oromia, Ethiopia |  |\n| G R A P H I C A L A B S T R A C T |  |\n| |  |\n\nA R T I C L E I N F O  \nKeywords:  \nMental health  \nEmotion detection  \nNatural language processing (NLP) BERT-base-uncased  \nModernBERT large  \nGoEmotions dataset  \nA B S T R A C T  \nAnxiety disorders, emotional conditions, and stress are becoming an increasing tendency in modern society, which makes digital mental health technologies that provide support in these cases highly demanded. This paper focuses on proposing an AI-driven framework that combines Natural Language Processing (NLP) with privacy-protective systems to recognize emotions in the text of the user. The AES-256 encryption, Supabase authentication, and role-based access control are used to secure data security. It is presented as a multi-label problem of emotion classification, which is fine-tuned on the BERT-base-uncased and ModernBERT Large transformer. ModernBERT  \n* Corresponding author.  \nE-mail address: [pooja.bagane@sitpune.edu.in](pooja.bagane@sitpune.edu.in) (P. Bagane).  \n[https://doi.org/10.1016/j.mex.2025.103756](https://doi.org/10.1016/j.mex.2025.103756)  \nReceived 2 August 2025; Accepted 9 December 2025  \nAvailable online 11 December 2025  \n2215-0161/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).  \nP. Bagane et al. MethodsX 16 (2026) 103756  \nMulti-label classification Binary cross entropy (BCE) loss Anonymous communication Mindfulness exercises Supabase  \nReact  \nTailwindCSS  \nSecure data handling  \nexhibited rapid convergence and enhanced situational sensitivity especially when it comes to sarcasm and mixed emotional states.  \nKey contributions include:  \n• Planning an emotion feedback loop offering contextual response and customized mindfulness teaching.  \n• Performing a Class imbalance analysis of emotions and trade-offs in precision and recall.  \n• Illustrating privacy-first architecture that is able to integrate with digital-therapy and clinician assisted decision software.  \n\n| Specifications table |  |\n| --- | --- |\n| Subject area | Computer Science |\n| More specific subject area | Artificial Intelligence |\n| Name of your method | BERT-base-uncased, ModernBERT Large |\n| Name and reference of original method | None |\n| Resource availability | None |\n\nBackground  \nModern living, augmented digital dependency, and socialization have become significant sources of emotional discomfort and the emerging issues of psychological concern across the globe. Even with the increasing awareness, stigma towards professional assistance and privacy issues usually dishearten people to get treatment or share their feelings. This highlights the necessity of safe, anonymous online communities where people could receive mental-health care without loss of confidentiality.  \nThe most critical aspect of it is Artificial Intelligence (AI), especially its Natural Language Processing (NLP) applications. NLP provides machines with the ability to recognize human emotion patterns. Transformer-based systems like BERT have been particularly beneficial to digital mental-health tasks, where the system needs to be aware of sub-communication like sarcasm, confusion, distress, etc., to respond appropriately. The privacy of users is ensured, unauthorised access is avoided, and the","cbCair5ribqjd3np","https://ap.wps.com/l/cbCair5ribqjd3np","pdf",2242372,12,"English","# Keywords\n## Abstract\n# Background\n## Privacy and NLP for emotion detection\n# Method details\n## Privacy-conscious transformer-based model","[{\"question\":\"What problem does the proposed AI framework address?\",\"answer\":\"It addresses the increasing need for digital mental-health support by recognizing emotions expressed in users’ text and providing early intervention support.\"},{\"question\":\"How does the framework preserve user privacy?\",\"answer\":\"It uses AES-256 encryption, Supabase authentication, and role-based access control to prevent unauthorized access and maintain confidentiality.\"},{\"question\":\"Which transformer models are used and how are they compared?\",\"answer\":\"The paper fine-tunes and compares BERT-base-uncased and ModernBERT Large for emotion detection, evaluating their suitability for mental-health scenarios.\"}]","AI-powered mental health application with data privacy preservation - Article | PDF",1790734407]