Research on Text Sentiment Analysis of Dual-channel Hybrid Neural Network Based on LERT 

Research on Text Sentiment Analysis of Dual-channel Hybrid Neural Network Based on LERT | 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 Research on Text Sentiment Analysis of Dual-channel Hybrid Neural Network Based on LERT Peng Ai, Qicheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4567953/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 current text sentiment analysis tasks, existing pre-trained language models are limited in fully grasping the intrinsic language features and deeply comprehending complex language structures. Classic neural network models struggle to adequately capture the semantic aspects of text. To address these challenges, this study introduces a novel dual-channel hybrid neural network approach for text sentiment analysis, leveraging the LERT model. This approach initially utilizes the advanced pre-trained language model LERT to generate dynamic word vectors from the text and subsequently captures both local and global semantic characteristics through a parallel dual-channel feature extraction layer. The features that have been extracted are merged and inputted into the fully connected layer, followed by the application of the softmax function to classify emotions. Results from experiments demonstrate that compared with other sentiment analysis models, the proposed model LDB-Net performs better in overall performance, validating the effectiveness of the proposed method. Sentiment Analysis LERT Pre-trained Language Model Feature Extraction Hybrid Neural Network 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-4567953","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317488365,"identity":"f06be29e-b079-4252-9010-b52f52feb377","order_by":0,"name":"Peng Ai","email":"","orcid":"","institution":"Yantai University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Ai","suffix":""},{"id":317488366,"identity":"69cffcf2-70ea-40ed-9945-034e2b25214c","order_by":1,"name":"Qicheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFAC5oYDQFKOjb39AJE62BjBWoz5eM4kEK8FRCXOk3AwIE6Hwf3GxsM8ZXbpbRIMCQw/KrYRoeUYY8NhnnPJuW3SjQcYe87cJlILb9uB3DaZAwnMjG0kaElnk0gwIE1LAvFaJI8lNhyccy7ZsA0YyAeJ8gvf4cOHP7wps5OXb28/+OBHBRFaFA6ASDYI5wBh9UAg34CkZRSMglEwCkYBVgAAnEs/YHiNLL0AAAAASUVORK5CYII=","orcid":"","institution":"Yantai University","correspondingAuthor":true,"prefix":"","firstName":"Qicheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-12 06:32:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4567953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4567953/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100373645,"identity":"9d6cc110-acf9-4f3e-a6c0-895a23a4735e","added_by":"auto","created_at":"2026-01-16 08:15:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":605168,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonTextSentimentAnalysisofDualchannelHybridNeuralNetworkBasedonLERT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4567953/v1_covered_a0fcd261-2ef6-450f-8b9c-9fc8c665343f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Text Sentiment Analysis of Dual-channel Hybrid Neural Network Based on LERT ","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":"Sentiment Analysis, LERT Pre-trained Language Model, Feature Extraction, Hybrid Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-4567953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4567953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn current text sentiment analysis tasks, existing pre-trained language models are limited in fully grasping the intrinsic language features and deeply comprehending complex language structures. Classic neural network models struggle to adequately capture the semantic aspects of text. To address these challenges, this study introduces a novel dual-channel hybrid neural network approach for text sentiment analysis, leveraging the LERT model. This approach initially utilizes the advanced pre-trained language model LERT to generate dynamic word vectors from the text and subsequently captures both local and global semantic characteristics through a parallel dual-channel feature extraction layer. The features that have been extracted are merged and inputted into the fully connected layer, followed by the application of the softmax function to classify emotions. 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