Adaptive SaaS Idea Validation: A Meta-Learning Approach Integrating Supervised Experts and Contextual Decision Policies

Adaptive SaaS Idea Validation: A Meta-Learning Approach Integrating Supervised Experts and Contextual Decision Policies | 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 Adaptive SaaS Idea Validation: A Meta-Learning Approach Integrating Supervised Experts and Contextual Decision Policies Tareq Hasan, Shibaditya Das, Riad Hossain, Ayesha Banu, Asbaul Yeamin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8898954/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 Software as a Service (SaaS) startups have an extremely high failure rate. Even though many business owners are adapting at creating goods, they frequently fail because they create goods that consumers do not genuinely want. It is essential to validate an idea before investing in its development. However, existing approaches, such as manual research or basic keyword tools are frequently out of date and unable to keep up with the rapid changes in social media public trends. We present the Adaptive SaaS Idea Validator, a novel system that employs artificial intelligence to forecast the success of a startup idea, in order to close this gap. Using the PRAW library, we gathered actual Reddit discussions to create a custom dataset. The sentiment (positive or negative emotions) and engagement levels of these conversations were then determine using a program called DistilBERT. To determine which AI model could most accurately predict success , we compared several different models. Conventional models that achieved accuracy included Random Forest (93.66%), Gradient Boosting (97.12%), and 1 LightGBM (97.12%). On the other hand, our suggested Offline Reinforcement Learning approach yielded the best results (97.23%). This method, in contrast to static models, makes better judgments about which concepts are feasible by learning from past data. We anticipate that this study will assist new business owners in lowering risk. Before devoting time and resources to creating the finished product, they can use this tool to test their concepts against actual social data. Software as a Service (SaaS) Startup Idea Validation Offline Reinforcement Learning Natural Language Processing (NLP) Social Media Mining Sentiment Analysis Predictive Analytics 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-8898954","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593887582,"identity":"8a76d10a-1df2-4a67-8c22-eb76bb673a88","order_by":0,"name":"Tareq Hasan","email":"","orcid":"","institution":"East Delta University","correspondingAuthor":false,"prefix":"","firstName":"Tareq","middleName":"","lastName":"Hasan","suffix":""},{"id":593887583,"identity":"718a50b3-fd81-45ef-8d97-54d76ba4fd8f","order_by":1,"name":"Shibaditya Das","email":"","orcid":"","institution":"East Delta University","correspondingAuthor":false,"prefix":"","firstName":"Shibaditya","middleName":"","lastName":"Das","suffix":""},{"id":593887584,"identity":"c4fe4eea-10d8-48c8-8e7a-4de633508a63","order_by":2,"name":"Riad Hossain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACZgYGCcYGGzBbgqEATBswMDYwE9KSBmZJMBgYEKGFAazlMAlazNuZH95g3HFejr///MEbHwz+yDOwN2+TYNxhjVOLzGE2YwvGM7eNJW4kM1vOMDAwbOA5VibBeCYdt6OYGcwkGNtuJzbcYGaT5jEwYGyQyAGJHMajhf0bUMG5+vnnD7NJ/zEwsG+Qf0NICw9IwYEEgwPJbNJA7yc2SPAQ1FJskdiWbLjxRrKxZY+BcXIbTxpQBJ9f+I9vvPGxzU5e7vzBhzd+VMjZ9rMfBorgCTEwSEDmsGGIjIJRMApGwSggGQAAUKhK/jgs3OYAAAAASUVORK5CYII=","orcid":"","institution":"East Delta University","correspondingAuthor":true,"prefix":"","firstName":"Riad","middleName":"","lastName":"Hossain","suffix":""},{"id":593887585,"identity":"b3c32fc9-a03e-4110-a338-b7e28b803c43","order_by":3,"name":"Ayesha Banu","email":"","orcid":"","institution":"Chittagong University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Banu","suffix":""},{"id":593887586,"identity":"4400e924-0579-4641-affa-6a99fce61fd8","order_by":4,"name":"Asbaul Yeamin","email":"","orcid":"","institution":"St. Edward's University","correspondingAuthor":false,"prefix":"","firstName":"Asbaul","middleName":"","lastName":"Yeamin","suffix":""},{"id":593887590,"identity":"7f89d701-87d2-4df9-b2be-9656f545532b","order_by":5,"name":"Mahfuzulhoq Chowdhury","email":"","orcid":"","institution":"Chittagong University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Mahfuzulhoq","middleName":"","lastName":"Chowdhury","suffix":""}],"badges":[],"createdAt":"2026-02-17 08:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8898954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8898954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507241,"identity":"68f0a9d6-823e-485d-8f2e-63c1e9447d2e","added_by":"auto","created_at":"2026-02-26 13:40:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1632921,"visible":true,"origin":"","legend":"","description":"","filename":"SaaSValidationFinal2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8898954/v1_covered_e321711a-0960-477e-a29b-dfe75e37bb81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive SaaS Idea Validation: A Meta-Learning Approach Integrating Supervised Experts and Contextual Decision Policies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Software as a Service (SaaS), Startup Idea Validation, Offline Reinforcement Learning, Natural Language Processing (NLP), Social Media Mining, Sentiment Analysis, Predictive Analytics","lastPublishedDoi":"10.21203/rs.3.rs-8898954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8898954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Software as a Service (SaaS) startups have an extremely high failure rate. 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