A Neutrosophy Framework for Emotion Analysis in Audio and Text Files using Harris Hawk Optimization

A Neutrosophy Framework for Emotion Analysis in Audio and Text Files using Harris Hawk Optimization | 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 A Neutrosophy Framework for Emotion Analysis in Audio and Text Files using Harris Hawk Optimization Iman Mousa, Shawkat K. Guirguis, Magda M. Madbouly This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4904568/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 In the digital data landscape, businesses are increasingly reliant on advanced tools for extracting actionable insights, with sentiment analysis being at the forefront. This study breaks new ground by exploring sentiment analysis in audio data through the lens of Neutrosophy. Our approach hinges on Harris Hawk Optimization (HHO) augmented with Neutrosophic Sets for efficient feature selection. Utilizing the Libri TTS train clean 100 dataset, our experiments demonstrate the superiority of our model in reducing feature numbers while enhancing sentiment analysis accuracy. Remarkably, the Neutrosophic Sets significantly boost HHO's performance, achieving a best fitness of 0.96, and enabling the selection of a minimal number of features (39 out of 300) with a rapid convergence rate. Classification accuracy, using SVM, KNN, and DT classifiers, shows a notable improvement over other fitness functions, particularly with Neutrosophic Sets, where accuracy increases to 0.96. Further, our model outperforms baseline algorithms in sentiment classification, with an 18% error rate reduction compared to the nearest competitor. Clustering analysis of the LibriSpeech dataset using Single-Valued Neutrosophic Sets reveals insightful patterns, underlining the model's potential to transform sentiment analysis by integrating both audio and textual data. This research not only advances sentiment analysis techniques but also pioneers the application of Neutrosophy in audio data analysis, offering novel insights and a robust framework for businesses and researchers in the digital era. Sentiment analysis Audio data text data Neutrosophic sets Clustering Optimization algorithms 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. 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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-4904568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345678966,"identity":"d74b1142-ad89-4107-9919-57e3d4f62d38","order_by":0,"name":"Iman Mousa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHAD5gMMDyAsA2K1sCUwJJCohceAOC38YoefPWDMsUmc337m44eEisOJDezN2yQYKmpxapGcnWZuwLgtLXHDmdzNEgln0hIbeI6VSTCcOY5Ti8HtBDMJxm2HEzcw5G6QSGyzSWyQyAGKtB3DqcX+dvo3sJb5/W8e/0j8J5HYIP8GqOUfbi0G0jkQWxpu5LAB1YNs4QGKNNTg1CJxO6dMInFbmvGGG8/MLBKOpRm38aQVAxkHcGrhn52+TeLjNhvZ+f3Jj298qDks289+eCOQUYdTCxgkIHPYICKH8WvBBgjYMgpGwSgYBSMJAABXqFauwDQzPQAAAABJRU5ErkJggg==","orcid":"","institution":"Alexandria University","correspondingAuthor":true,"prefix":"","firstName":"Iman","middleName":"","lastName":"Mousa","suffix":""},{"id":345678967,"identity":"5665e220-4541-4cac-b6f1-e3f73afa73de","order_by":1,"name":"Shawkat K. 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