[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125576-en":3,"doc-seo-125576-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125576,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning of Public Sentiments toward Wind Energy in Norway - A Preprint","Europe’s negative public opinion can constrain the deployment of renewable energy infrastructure needed for net-zero transitions. Understanding how sentiment varies across space and time is critical for socially accepted energy decision-making. This preprint applies the NorBERT sentiment classification model to Twitter data from 2006–2022 to analyze opposition to wind power in Norway. Results show intensified discussion in 2018/2019 and increasingly negative tweets until 2020, with weak geographic clustering.","MACHINE LEARNING OF PUBLIC SENTIMENTS TOWARD WIND  \nENERGY IN NORWAY  \nA PREPRINT  \narXiv :2304 .02388v1 [ stat .AP] 5 Apr 2023  \n Oskar Vågerö􀀃  \nDepartment of Technology Systems University of Oslo Kjeller, Norway [oskar.vagero@its.uio.no](oskar.vagero@its.uio.no)  \nAnders Bråte  \nDepartment of Physics University of Oslo Oslo, Norway  \n[anders.brate@fys.uio.no](anders.brate@fys.uio.no)  \nAlexandra Wittemann  \nDepartment of Informatics University of Oslo Oslo, Norway [alexankw@ifi.uio.no](alexankw@ifi.uio.no)  \nJessica Yarin Robinson  \nDepartment of Media and Communications University of Oslo Oslo, Norway [j.y.robinson@media.uio.no](j.y.robinson@media.uio.no)  \nNatalia Sirotko-Sibirskaya  \nDepartment of Mathematics University of Oslo Oslo, Norway  \n[nsibirska@math.uio.no](nsibirska@math.uio.no)  \n Marianne Zeyringer  \nDepartment of Technology Systems  \nUniversity of Oslo  \nKjeller, Norway  \n[marianne.zeyringer@its.uio.no](marianne.zeyringer@its.uio.no)  \nApril 4, 2023  \nABSTRACT  \nAcross Europe negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net-zero energy systems. Understanding public sentiment and its spatio-temporal variations is as such important for decision-making and socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allows for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment.  \nKeywords Wind power 􀀁 Machine learning 􀀁 Sentiment analysis 􀀁 Twitter 􀀁 Public sentiment  \n􀀃 Corresponding Author  \n1 Introduction  \nWind power technology is considered key for the transition to net-zero energy systems [Nordic Energy Research, 2016, IRENA, 2020, IEA, 2022, European Commission, 2022] and to increase Europe's energy independence, yet it is also contested in many countries, including the UK [Roddis et al., 2018], Denmark [Ladenburg et al., 2020] and Norway [Normann, 2021] . Negative public sentiment toward wind energy can act as an impediment to developments that are otherwise techno-economically viable [Devine-Wright, 2005, Johansson and Laike, 2007, Firestone et al., 2009] . The importance of wind energy to meet climate targets as well as responding to the energy crisis in Europe thus makes it important to understand public sentiments.  \nThe role of public acceptance of onshore wind power has been a topic of research ever since the early 1980s [Wüstenhagen et al., 2007] and studies have attempted to identify drivers of acceptance/opposition, such as national and local ownership and use [Warren and McFadyen, 2010, Brennan et al., 2017, Linnerud et al., 2022, Vuichard et al., 2022], exposure and proximity to the wind power plants [Brennan and Van Rensburg, 2016, Dugstad et al., 2020] and associated ecological impacts [Vuichard et al., 2022] . Results are not always consistent and while some indicate that for example exposure to wind power plants lead to higher acceptance [Liebe et al., 2017], others show the opposite [Dugstad et al., 2020] . Recent research have highlighted geographical difference and the need for more cross-country comparisons [Vuichard et al., 202","cbCaikvos0YSB1NG","https://ap.wps.com/l/cbCaikvos0YSB1NG","pdf",22593149,1,31,"English","en",105,"# Introduction\n## Public acceptance and opposition to wind energy\n## Limitations of traditional survey methods\n## NLP and social media for sentiment analysis\n# Methods and data approach","[{\"question\":\"What study question does the document address?\",\"answer\":\"It investigates how public sentiment toward wind energy in Norway varies across time and space.\"},{\"question\":\"What data and model are used?\",\"answer\":\"The study uses Twitter posts with geospatial information from 2006 to 2022 and applies the NorBERT sentiment classification model for Norwegian.\"},{\"question\":\"What major patterns are found in opposition to wind power?\",\"answer\":\"Discussion intensifies in 2018/2019, and tweets become more negative up to 2020, with only weak geographical clustering overall.\"}]","Machine Learning of Public Sentiments toward Wind Energy in Norway - A Preprint | PDF",1785899977,78,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-of-public-sentiments-toward-wind-energy-in-norway-a-preprint","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-of-public-sentiments-toward-wind-energy-in-norway-a-preprint/125576/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What study question does the document address?","Question",{"text":75,"@type":76},"It investigates how public sentiment toward wind energy in Norway varies across time and space.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and model are used?",{"text":80,"@type":76},"The study uses Twitter posts with geospatial information from 2006 to 2022 and applies the NorBERT sentiment classification model for Norwegian.",{"name":82,"@type":73,"acceptedAnswer":83},"What major patterns are found in opposition to wind power?",{"text":84,"@type":76},"Discussion intensifies in 2018/2019, and tweets become more negative up to 2020, with only weak geographical clustering overall.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]