Sentiment Analysis of Solar Energy in U.S. Cities: A 10-Year Analysis Using Transformer-Based Deep Learning

Sentiment Analysis of Solar Energy in U.S. Cities: A 10-Year Analysis Using Transformer-Based Deep Learning | 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 of Solar Energy in U.S. Cities: A 10-Year Analysis Using Transformer-Based Deep Learning Serena Kim, Crystal Soderman, Lan Sang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5050458/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 This study examines U.S. public sentiment toward solar energy from 2013 to 2022, analyzing 8 million social media posts using RoBERTa, a transformer-based deep learning algorithm. Sentiment has generally been positive but has declined since 2016 due to increasing negativity. Significant regional and state differences exist, with these disparities deepening over the decade, suggesting growing polarization in views toward solar energy. Negative sentiment is more pronounced in Republican-leaning and Southern U.S. regions. A two-way fixed-effects panel analysis at the municipality level shows positive sentiment in areas with high solar radiation and a large remote-working population, while negative sentiment is more common in regions with high wind speeds and significant multifamily housing. These findings underscore the complex interactions among environmental, socioeconomic, political, and technological factors, highlighting the need for tailored strategies to address regional and demographic disparities in solar energy sentiment. The computational approach developed and tested in this study, which leverages natural language processing and geospatial analysis of social media data, offers broader applicability and provides a scalable framework for analyzing sentiment across various regions and topics beyond the specific context of solar energy in the United States. text analytics renewable energy computational linguistics artificial intelligence sentiment analysis Full Text Additional Declarations The authors declare no competing interests. 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. 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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-5050458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350937574,"identity":"4c843da6-f520-4038-b75c-e0ff158e2cf4","order_by":0,"name":"Serena Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBADAzYG5gNQdgLRWthgSonVwsDAY0CcFvn23oePC2oYjPnEznx78OPPYQZ+9hwDvFoMzhw3Np5xjMGMTTp3u2Fv22EGyZ43BLRIpLFJ87Ax2AC1bJNmbDjMYHCDgC3y85+x/+b5B9KS80yaAegwe0JaGG6wsTHztoEclsMmzcAGtEWCoF/SmKV5+xiM2aTTzCR729J5JM48K8DvsPZjjJ95vjEYzp+d/Ezixx9rOf725A34HQYB/+EsHmKUj4JRMApGwSggAAClwTqf9KGi/gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3839-3674","institution":"North Carolina State University","correspondingAuthor":true,"prefix":"","firstName":"Serena","middleName":"","lastName":"Kim","suffix":""},{"id":350937575,"identity":"be4d47f9-c33e-4e11-9538-2da2122ba083","order_by":1,"name":"Crystal Soderman","email":"","orcid":"","institution":"University of Colorado Denver","correspondingAuthor":false,"prefix":"","firstName":"Crystal","middleName":"","lastName":"Soderman","suffix":""},{"id":350937576,"identity":"8a2c4584-6873-498d-86f4-48c297af4871","order_by":2,"name":"Lan Sang","email":"","orcid":"","institution":"University of Colorado Boulder","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Sang","suffix":""}],"badges":[],"createdAt":"2024-09-07 22:09:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5050458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5050458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64201877,"identity":"63fb038c-d9d6-4cbb-b84c-7415ad92a1d9","added_by":"auto","created_at":"2024-09-10 02:42:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1830919,"visible":true,"origin":"","legend":"","description":"","filename":"SolarSentimentJofCompSocSci1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5050458/v1_covered_768b2027-dc17-4dcc-9e82-90129029cc87.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSentiment Analysis of Solar Energy in U.S. Cities: A 10-Year Analysis Using Transformer-Based Deep Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"North Carolina State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"text analytics, renewable energy, computational linguistics, artificial intelligence, sentiment analysis","lastPublishedDoi":"10.21203/rs.3.rs-5050458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5050458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines U.S. public sentiment toward solar energy from 2013 to 2022, analyzing 8 million social media posts using RoBERTa, a transformer-based deep learning algorithm. 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