Enhancing Mobile App Ranking through Trust-Based Sentiment Analysis using Large Language Models

Enhancing Mobile App Ranking through Trust-Based Sentiment Analysis using Large Language Models | 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 Enhancing Mobile App Ranking through Trust-Based Sentiment Analysis using Large Language Models Muhammad Hassam Aslam khan, Rajeev R. Raje This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8943112/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Selecting the “most suitable” app from a categoryremains a challenge due to the presence of multiple highly ratedalternatives offering similar functionality. Users often rely ontrial and error, including installing and testing several apps,before finding one that meets their needs. In this study, weextend previous research conducted by our group and propose anenhanced trust-based app ranking approach that takes advantageof user feedback available in the “Relevant” section of GooglePlay reviews. We collect apps from a specific category and analyzeuser reviews using three different large language models (LLMs)to extract sentiment data. From these sentiment scores, we derivea BDU tuple, representing Belief, Disbelief, and Uncertaintyabout the app. We use the BDU tuple to compute a trustscore that reflects users’ perceived trust about each app. Appsare then ranked based on these trust scores. We compare ourranking with existing app ratings, available in Google Play, usingthe Kendall Tau distance. Our approach offers a user-centricalternative to traditional rating systems by emphasizing perceivedtrustworthiness derived from real user experiences. Mistral LLaMA Gemini BDU Google Play Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 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-8943112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599584308,"identity":"a847e447-63a3-4b65-b397-c6915def61ad","order_by":0,"name":"Muhammad Hassam Aslam khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACCQkgkVBhwwPmEK/lwZk0ErUwPmw7zEC8FsnZzc8eJLadlzE4wHzwNg8xWqRljpkbJJy7zWNwgC3ZmigtchIJZhIJZSAtPGbSRGpJ/yaRwHYOqIX/G3FapCVygLa0HQDZwkacFskZOWUSCWeSeSQPsxlbziFGi8SN9G2SPyrs7PmONz+88YYYLQjATJryUTAKRsEoGAX4AADwGyzR9Pv2nQAAAABJRU5ErkJggg==","orcid":"","institution":"Indiana University Indianapolis Indiana","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Hassam Aslam","lastName":"khan","suffix":""},{"id":599584309,"identity":"1890c8ca-a988-4180-80c3-f1138f655ffd","order_by":1,"name":"Rajeev R. 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