Sentiment AI : Extraction and Analysis From Webblogs Using Python

Sentiment AI : Extraction and Analysis From Webblogs Using Python | 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 Analysis Sentiment AI : Extraction and Analysis From Webblogs Using Python SANDEEP BHATTACHARJEE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4794397/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 detection is a key to real time identification of human sentiments. Sentiment detection towards the recent developments in artificial intelligence holds the key for developments in all industries where applications of artificial intelligence exist or may exist in future. With the growing number of complexities in artificial intelligence applications for varied results, the onus lies on understanding the trends of the research community to detect the movement of development in different paths of development. Many existing methods can be used to identify the sentiments of researchers and learners in artificial intelligence. Several of such methods do exist to cater to such necessities. In this research paper, four methods namely word count, word space, word correlation and similarity algorithm, have been tested using python 3.10.13 codes on seven web sources which excels in discussions on research and developments in artificial intelligence. Scientific community and society/Business and industry/Engineering Scientific community and society/Business and industry algorithm content count text word 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. 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-4794397","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":331387533,"identity":"22280a8f-ab5d-4185-9f14-50d5a1db2ee8","order_by":0,"name":"SANDEEP BHATTACHARJEE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYJACCSBOYGNgAKIKIJOZuYFILWwgLWdAWhiJ1MIA0sLYBuIT0CLffvbgjQ8Vd/L45LvTHvycVxvN3w7U8qNiG04tBmfyki1nnHlWzMbGu92wd9vx3BmHGRsYe87cxq2FIcdMmrftcGIbG+82Cd5tx3IbgFqYGdtwa5Hvf2Mm/fcfRIvk3znHcucT0sJwA2gLYwNEizRvQ03uBkJaDG68MbbsOQbyS+52Y5ljB3I3ArUcxOcX+f4cwxs/au7kyTef3fbwTU1d7rzzhw8++FGBx2EQcADGOIzKJUZLHRGKR8EoGAWjYKQBAL8gXvzf9V3zAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6686-3947","institution":"AMITY UNIVERSITY KOLKATA","correspondingAuthor":true,"prefix":"","firstName":"SANDEEP","middleName":"","lastName":"BHATTACHARJEE","suffix":""}],"badges":[],"createdAt":"2024-07-24 10:04:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4794397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4794397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84521078,"identity":"a91ee52b-5038-4726-a4ac-429dbd81ccec","added_by":"auto","created_at":"2025-06-13 03:27:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":719503,"visible":true,"origin":"","legend":"","description":"","filename":"SENTIMENTAIUSINGPYTHONAN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4794397/v1_covered_72c563b6-09d6-4df2-9f6a-af189339f7fb.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eSentiment AI : Extraction and Analysis From Webblogs Using Python\u003c/p\u003e","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":"algorithm, content, count, text, word","lastPublishedDoi":"10.21203/rs.3.rs-4794397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4794397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSentiment detection is a key to real time identification of human sentiments. 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