Sentiment Analysis with Graph Convolutional Networks using directed PMI

Sentiment Analysis with Graph Convolutional Networks using directed PMI | 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 Sentiment Analysis with Graph Convolutional Networks using directed PMI Mina Ameripour, Vahid Seydi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4564482/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 is a fast-growing area of research in natural language processing (NLP) and more specifically text classification which makes it possible to automatically detect the sentiment of data and is used in a wide range of applications. Many studies have applied deep learning techniques to text classification trying to mend the shortcomings of previous methods and improve their performance. In recent years several studies have applied the powerful graph convolutional networks (GCNs) to text classification problems, demonstrating promising results. However, most GCNs can’t directly work on directed graphs and make full use of the informative graph structure and these models’ capabilities. As order is extremely important in sentiment analysis tasks and most datasets in this field contain short texts, considering the order of words and creating a directed graph can add extra information to model and improve the training process. In this study we introduce DPMI (Directed PMI) as a measure to calculate word associations between word nodes considering the order of word occurrence and enhance one of the powerful existing GCN models named Text GCN, to accept directed graphs. The results show that adding order and using the proper aggregation function can improve the accuracy of predictions without adding complexity to the model. Sentiment analysis Graph Convolutional network (GCN) Directed PMI order 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-4564482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313347199,"identity":"b393ab7a-b31f-4171-9ab5-5a856db3227b","order_by":0,"name":"Mina Ameripour","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Ameripour","suffix":""},{"id":313347200,"identity":"04a837af-83af-459b-a217-01954ec3abf8","order_by":1,"name":"Vahid Seydi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACgwPsDxgY2CTkDJAEmQlo4TEAaTEmSQuQZGNI3EC0FskGHsbHPGUW6dvZzx5+8XEPgzx/Aw+KlVi0sD825jknkbuzJy/NcsYzBsMZB3iME/BrYTCT5m2TyN1wIMfMmOcAA+MGBh7jA/i08DMwmP8Gakk3OP8GrMWeGC1mzEAtCQY3cowfA7UkgrTgdRgbM4+x5JxzEoY7Z7wxY5xxQCJ5xmG2YrzeZ2Nvf/jhTVmdvDl/jvGHDwdsbPvbmzdL4NMCigMmHqh2oEoJQrECAYw/oLo/EKF4FIyCUTAKRiAAAAZrQZxJSgeHAAAAAElFTkSuQmCC","orcid":"","institution":"Bangor University","correspondingAuthor":true,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Seydi","suffix":""}],"badges":[],"createdAt":"2024-06-11 13:27:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4564482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4564482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59336854,"identity":"f521fb6e-dac4-4e44-b7ac-25e2008dcf80","added_by":"auto","created_at":"2024-06-30 01:16:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":625877,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4564482/v1_covered_6a6dcb79-0e20-4c59-a43f-eb92e1d8540d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sentiment Analysis with Graph Convolutional Networks using directed PMI","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, Graph Convolutional network (GCN), Directed PMI, order","lastPublishedDoi":"10.21203/rs.3.rs-4564482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4564482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Sentiment analysis is a fast-growing area of research in natural language processing (NLP) and more specifically text classification which makes it possible to automatically detect the sentiment of data and is used in a wide range of applications. Many studies have applied deep learning techniques to text classification trying to mend the shortcomings of previous methods and improve their performance. In recent years several studies have applied the powerful graph convolutional networks (GCNs) to text classification problems, demonstrating promising results. However, most GCNs can’t directly work on directed graphs and make full use of the informative graph structure and these models’ capabilities. As order is extremely important in sentiment analysis tasks and most datasets in this field contain short texts, considering the order of words and creating a directed graph can add extra information to model and improve the training process. In this study we introduce DPMI (Directed PMI) as a measure to calculate word associations between word nodes considering the order of word occurrence and enhance one of the powerful existing GCN models named Text GCN, to accept directed graphs. The results show that adding order and using the proper aggregation function can improve the accuracy of predictions without adding complexity to the model.","manuscriptTitle":"Sentiment Analysis with Graph Convolutional Networks using directed PMI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 17:36:41","doi":"10.21203/rs.3.rs-4564482/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"255ef90f-ce34-44f3-89c2-6dc1ad06b1e7","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-30T01:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 17:36:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4564482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4564482","identity":"rs-4564482","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}