Big Data-Driven Deep Learning for Natural Language Processing

Big Data-Driven Deep Learning for Natural Language Processing | 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PDF Article Big Data-Driven Deep Learning for Natural Language Processing rakshit dabral This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5413571/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 Sentiment analysis, a crucial task in natural language processing (NLP), aims to extract and classify sentiments expressed in textual data. This research delves into the application of deep learning techniques, powered by Big Data, to enhance sentiment analysis accuracy. By leveraging a substantial Amazon review dataset, we train a simple feedforward neural network to classify sentiments as positive or negative. The model employs embedding layers to represent words as dense vectors, followed by a global average pooling layer to capture semantic information. A final dense layer with a sigmoid activation function predicts the sentiment probability. The results demonstrate the effectiveness of deep learning in capturing complex linguistic nuances and achieving high accuracy. With an accuracy of 88.47%, the model outperforms traditional methods, showcasing the potential of Big Data and deep learning in sentiment analysis. Future research directions include exploring more sophisticated architectures, addressing class imbalance issues, improving model interpretability, and incorporating domain-specific knowledge to further enhance sentiment analysis performance. Physical sciences/Mathematics and computing/Software Scientific community and society/Developing world Sentiment Analysis Keras Tensor flow Hadoop Deep Learning Lemmatization Vectorization Stemming Full Text Additional Declarations There is NO Competing Interest. 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-5413571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377656757,"identity":"51fb1c0b-1552-41ac-a7e7-14ccdc5a55a1","order_by":0,"name":"rakshit dabral","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFACNgZmBgMGxgYJIPuBgQ2QZGw8QIQWA4iWhII0kJYGIrQwwLR8OAwWw6vFvP1Y8ueCgj+y/bN7H35IMDhvt7b9MNCWGptoXFpkzqQdk55hYGA8485xY4kEg9vJ284kArUcS8ttwKFFgiG9jZnHwCCx4UYaA1iL2QGgFsaGw7i18D9v/gzSMv9GGvOPBINzyWbnHxLQIpF2QBqkZcONNDagLQfszG4QskXiWRpQi7HxxjvH2CwSDJITzG4AbUnA5xf+NOPPPH/kZOfdbmO+8eGPnb3Z+fSHDz7U2ODUggESwSoTiFUOAvakKB4Fo2AUjIKRAQCthmMlg8lIWwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-2940-1542","institution":"Dr. Akhilesh Das Gupta Institute of Professional Studies","correspondingAuthor":true,"prefix":"","firstName":"rakshit","middleName":"","lastName":"dabral","suffix":""}],"badges":[],"createdAt":"2024-11-08 04:40:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5413571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5413571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70687196,"identity":"65b0f799-9128-494b-b4f4-c6cba4f0f367","added_by":"auto","created_at":"2024-12-05 15:47:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":266390,"visible":true,"origin":"","legend":"","description":"","filename":"researchpaperfinal1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5413571/v1_covered_8132a250-e271-4b11-8c3d-12dc8c7d8954.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Big Data-Driven Deep Learning for Natural Language Processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, Keras, Tensor flow, Hadoop, Deep Learning, Lemmatization, Vectorization, Stemming","lastPublishedDoi":"10.21203/rs.3.rs-5413571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5413571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSentiment analysis, a crucial task in natural language processing (NLP), aims to extract and classify sentiments expressed in textual data. 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