A Novel Hybrid Fuzzy-WASPAS-ART Framework for Evaluating Emotional Intelligence and Communication Effectiveness Using LLM Empathy and BERT Deep Features

A Novel Hybrid Fuzzy-WASPAS-ART Framework for Evaluating Emotional Intelligence and Communication Effectiveness Using LLM Empathy and BERT Deep Features | 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 A Novel Hybrid Fuzzy-WASPAS-ART Framework for Evaluating Emotional Intelligence and Communication Effectiveness Using LLM Empathy and BERT Deep Features Juan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6806930/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 Evaluating emotional intelligence (EI) and communication ability from digital text is becoming increasingly crucial in education, mental health, and human-computer interaction. The present project offers a novel hybrid framework that combines fuzzy decision-making, sentiment analysis, empathy scoring, and deep semantic feature extraction into a comprehensive assessment of EI in written communication. Features from textual data are generated with BERT embeddings and reduced with principal component analysis (PCA), while VADER scores normalize sentiment and a LLaMA-based large language model score provides empathy scores. Fuzzy logic with triangular membership functions addresses the inherent uncertainty associated with emotional expression and translates the numerical scores into linguistic categories that can be more easily interpreted. The framework facilitates multi-criteria decision-making using the Weighted Aggregated Sum Product Assessment (WASPAS)-method and allows for integrated scores of sentiments, empathy, and semantic features. Results from our evaluation of a large emotion-labeled corpus suggest that empathy, as assessed by advanced language models, is the variable with the most significant negative impact on communication quality over and above sentiment. The framework allows for interpretable, easily scalable and actionable approaches to next-generation emotion-aware systems and informs the development and deployment of emotionally intelligent technology in the real world by opening avenues for benchmarking. Biological sciences/Psychology Physical sciences/Engineering Physical sciences/Mathematics and computing Emotional intelligence Communication skills WASPAS Fuzzy decision making 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. 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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-6806930","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471150763,"identity":"0ae42a47-387a-49a1-8f9a-a0f5b0f4188b","order_by":0,"name":"Juan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBAC9gbmBhAtx8be2PggocKGsBaeA4xgLcb8PIcPGzw4k0a8lsSZM9LSJB+2HSJCi0Ri4+fCNjvGDQdyzCoS2A4w8Ld3JxDS0iw9sy2Z2eDAGbMbCTx3GCTOnN2AV4u9RGKDNG8bM5vBwR6gFolnDAYSufi1gGz5zdtWz2NwmMesIMHgMFFa2oC2HJaQbGNLY0hIIEYLz8M2a55zxw34eZgPSyQcSOMh6Bce9uTDt3nKquvb5B82fvz5z0aOv70XvxYwYGRDMoOwcjD4Q6S6UTAKRsEoGJkAAPoqSmt2HpogAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-03 04:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6806930/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6806930/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90824176,"identity":"0b36567d-e011-4fad-a632-01b345f96525","added_by":"auto","created_at":"2025-09-08 15:02:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1282663,"visible":true,"origin":"","legend":"","description":"","filename":"WASPAS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6806930/v1_covered_c38eda2a-931c-4685-b327-be57fa72289b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Hybrid Fuzzy-WASPAS-ART Framework for Evaluating Emotional Intelligence and Communication Effectiveness Using LLM Empathy and BERT Deep Features","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":"Emotional intelligence, Communication skills, WASPAS, Fuzzy decision making","lastPublishedDoi":"10.21203/rs.3.rs-6806930/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6806930/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Evaluating emotional intelligence (EI) and communication ability from digital text is becoming increasingly crucial in education, mental health, and human-computer interaction. 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