Advanced deep learning technology for emotion analysis on social media platform

Advanced deep learning technology for emotion analysis on social media platform | 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 Advanced deep learning technology for emotion analysis on social media platform Chunyang Shi, Yue Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6177247/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 When people want to express themselves online, microblogs are among the most popular platforms. Thus, emotion identification from microblogs has become a fascinating area of study for several reasons. Some problems with social media sentiment analysis include relying too much on human annotation, not capturing feature words having emotional hue well, and ignoring the long-distance semantic relationship of emotional characteristics. In order to accomplish the emotional evaluation of public opinion events on microblogs, this study presents a model for user emotion identification. The data collection and preparation of micro-blog comment language from public opinion events yields three distinct kinds of motivating text: "joy," "anger," and "sadness." Words describing emotions are then extracted by means of an algorithm that incorporates the Linear Discriminant Analysis (LDA) approach, an emotion dictionary, and human annotation. Using FastText, the inspiring text that was collected is transformed into a word vector. To complete the emotion classification, extract the text's main properties after collecting long-distance semantic data utilizing Siamese Capsule Networks. The test findings show that six machine learning models had an average F1 value increase of 4% and seven deep learning models had an average F1 value increase of 3%. When compared to existing machine learning as well as deep learning approaches, the proposed model outperforms them when it comes to determining the emotional state of social media users. Deep learning Machine learning social media Sentiment characteristics Emotional analysis 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-6177247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441572482,"identity":"cfaabde0-5e7c-4040-a9e7-861ae79c0141","order_by":0,"name":"Chunyang Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3PsQqDMBCA4RNBl1TpZin4DJVAXPIwccnUpZtjQNCl0NXHEPoCQqhd8gAOHZw627GLVO3YonbrkH87uA/uAHS6P8xOwAIWd747TA3AbpYYI1El3ggwBFtERE+MtIyKcjHJVhU01g3j6zFsWEwx2PJSTB/mcGDo7hOlAsEUJ4A4r6cJIsA8E5N6H4golRQ8RBaQnRmd86VkPRImo8J7EzJLtuZASo49VR3y/hdszf3iuCp8PDvqu1lStG1Mg5Mtq0nymfXbuk6n0+m+9QKl8UhPBtrzIQAAAABJRU5ErkJggg==","orcid":"","institution":"BaiCheng Normal University","correspondingAuthor":true,"prefix":"","firstName":"Chunyang","middleName":"","lastName":"Shi","suffix":""},{"id":441572483,"identity":"63e90e0c-ca41-461d-994b-8759a7743921","order_by":1,"name":"Yue Li","email":"","orcid":"","institution":"BaiCheng Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-07 10:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6177247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6177247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89533799,"identity":"71f84999-0a26-4ede-85ad-2447d4e0176a","added_by":"auto","created_at":"2025-08-21 04:53:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":341407,"visible":true,"origin":"","legend":"","description":"","filename":"Advanceddeeplearningtechnologyforemotionanalysisonsocialmediaplatform1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6177247/v1_covered_666a7188-2a4c-45ab-8bdb-c9f44eb0fcd5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced deep learning technology for emotion analysis on social media platform","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":"Deep learning, Machine learning, social media, Sentiment characteristics, Emotional analysis","lastPublishedDoi":"10.21203/rs.3.rs-6177247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6177247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhen people want to express themselves online, microblogs are among the most popular platforms. 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