Decoding Student Satisfaction: A Machine Learning Approach to Opinion Mining in Educational Feedback

Decoding Student Satisfaction: A Machine Learning Approach to Opinion Mining in Educational Feedback | 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 Decoding Student Satisfaction: A Machine Learning Approach to Opinion Mining in Educational Feedback Alvia Fatima, Ubaida Fatima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9330096/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 students’ feedback and reviews continue to gain prominence in informing academic decisions, there is a pressing need to assess student sentiments and opinions regarding their academic journeys. This study aims to build an opinion mining framework to assess and analyze student feedback regarding educational institutions, focusing on student satisfaction. By using machine learning and natural language processing techniques, this research explores extracting and classifying student opinions from feedback and converting them into the count of positive, negative, and neutral opinions. In this study, three machine learning algorithms, namely Naïve Bayes, Support Vector Machines, and Logistic Regression, were employed with Term Frequency- Inverse Document Frequency (TF-IDF) and Bag of Words vectorizers to analyze the sentiments. The classification models were compared using evaluation parameters. The findings of this study provide that SVM with Bag of Words performed well and correctly classified the students’ sentiment as compared to other models. Physical sciences/Engineering Physical sciences/Mathematics and computing Sentiment analysis opinion mining students’ satisfaction Support Vector Machine Full Text Additional Declarations No competing interests reported. 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-9330096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":629694859,"identity":"1bffff15-0968-4a2f-b154-6f3b62f12aca","order_by":0,"name":"Alvia Fatima","email":"","orcid":"","institution":"NED University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Alvia","middleName":"","lastName":"Fatima","suffix":""},{"id":629694860,"identity":"25c84da5-a57d-403a-b591-ba23b8597ae2","order_by":1,"name":"Ubaida Fatima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACCQaGAzwMNkBWApjP2MDAwEyMljSIlgPEamHgYThMghaD280PD7ypOZ/Hz55j+PgDg43shgO8hw3warlzzODgnGO3iyV73hgbHGBIM95wgC85Aa+WGwkGh3nYbiduuJFjJnGA4XDihgM8xgfwa0n/cJjn37nE/TdyzH8cYPhPjJYcg8O8bQcSN0jkmAG9fwCsBa/DJO+cKTg4ty85ccaZZ8USZwySjWce5kvG632+2+2bP7z5ZpfY35688UNFhZ1s3/HewxL4tKC7E4iZeUjQAAVkaBkFo2AUjIJhDQC1G1a3lyMfRAAAAABJRU5ErkJggg==","orcid":"","institution":"NED University of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ubaida","middleName":"","lastName":"Fatima","suffix":""}],"badges":[],"createdAt":"2026-04-06 05:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9330096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9330096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109425881,"identity":"41529232-49bf-41a8-a313-07804f2ee764","added_by":"auto","created_at":"2026-05-18 02:40:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":722216,"visible":true,"origin":"","legend":"","description":"","filename":"AUPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9330096/v1_covered_8f4c8cee-58fd-4538-a8ae-c02c5ae545d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decoding Student Satisfaction: A Machine Learning Approach to Opinion Mining in Educational Feedback","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":"Sentiment analysis, opinion mining, students’ satisfaction, Support Vector Machine","lastPublishedDoi":"10.21203/rs.3.rs-9330096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9330096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As students’ feedback and reviews continue to gain prominence in informing academic decisions, there is a pressing need to assess student sentiments and opinions regarding their academic journeys. 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