Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Smart Urban Environments

Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Smart Urban Environments | 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 Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Smart Urban Environments Gayatri Parasa, Siva Shankar S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4451396/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 smart urban environments become increasingly pivotal in the realm of health crisis management, there is a growing need for sophisticated sentiment analysis algorithms capable of understanding and responding to the dynamic nature of public sentiment during crises. This paper proposes a novel approach that integrates Context-Aware Sentiment Analysis using Reinforcement Learning (CASARL) to improve sentiment analysis's precision as well as flexibility when applied to health emergencies in smart city environments. This approach makes use of deep learning architectures, including Transformer-based models or Long Short-Term Memory (LSTM), to extract intricate contextual information from text data pertaining to illnesses. The algorithm learns optimal sentiment analysis actions through a reward-based system that considers the real-world impact of sentiment classifications during health crises. By taking into consideration the particular circumstances, happenings, and attitudes that are common in smart communities during health emergencies, the suggested method seeks to deal with the shortcomings of conventional sentiment analysis methods. The integration of reinforcement learning ensures adaptability to evolving contexts, allowing the model to dynamically adjust sentiment analysis strategies based on real-time data. The effectiveness of our approach is evaluated through extensive simulations and experiments using historical health crisis data and real-time data from smart urban environments. The findings highlight how the suggested CASARL technique outperforms more conventional sentiment analysis algorithms in terms of accuracy and flexibility, suggesting that it could be a useful instrument for managing health crises in smart urban environments. Parameter tuning Sentiment analysis smart city healthcare management artificial intelligence metaheuristics deep learning Full Text 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-4451396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495548514,"identity":"b9a649ac-184b-4013-b068-314f3846cee4","order_by":0,"name":"Gayatri Parasa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABG0lEQVRIiWNgGAWjYBAC/gYwJQHlGrDJgagDD/BokTgAUgHXUsBnDNaSgEeLAVgFnPtBLhFsLV4t7GcMP3/cY5HHP/vw4w8/DMzS54cdfgi0xU5OtwGHFp4cY4kDzySKJc6lmUn2GKTlbrydZgDUkmxsdgC7FsMDOQYSBw5IJDacYTBj4DE4lrtxdgJIy4HEbTi0GJx/Y/wDpGX+GfbPH/8Y/E83nJ3+Ab+WGzlmYFs2nOExkOYxYEuQl87Bb4vEjWdlFmcOSBQbnuEpk5YxYDPcIJ1TcCDBALdf+PuTN9+oOFCXJ3eGffPHN3/Y5OVnp2/+8KHCTg6XFgYGDnDUJCCcClZpgEs5CLA/QNUi34BP9SgYBaNgFIxEAABAjmgS7XUs6AAAAABJRU5ErkJggg==","orcid":"","institution":"Annamalai University","correspondingAuthor":true,"prefix":"","firstName":"Gayatri","middleName":"","lastName":"Parasa","suffix":""},{"id":495548515,"identity":"532f5797-3278-4b01-b66a-e03ef4224d61","order_by":1,"name":"Siva Shankar S","email":"","orcid":"","institution":"KG Reddy College of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Siva","middleName":"Shankar","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-05-21 00:58:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4451396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4451396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89230066,"identity":"02f574bb-7447-48ac-8d3d-a4c105bbc04e","added_by":"auto","created_at":"2025-08-17 14:10:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":513539,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4451396/v1_covered_1e1596d3-bc0f-4f1d-b218-b7b7aa7d45e8.pdf"}],"financialInterests":"","formattedTitle":"Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Smart Urban Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Parameter tuning, Sentiment analysis, smart city, healthcare management, artificial intelligence, metaheuristics, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-4451396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4451396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs smart urban environments become increasingly pivotal in the realm of health crisis management, there is a growing need for sophisticated sentiment analysis algorithms capable of understanding and responding to the dynamic nature of public sentiment during crises. 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