Across 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.

翻译：在欧洲，负面公众舆论已经并将可能继续限制向净零能源系统转型所需的可再生能源基础设施的部署。因此，理解公众情绪及其时空变化对于决策和社会可接受的能源系统具有重要意义。本研究基于自然语言处理的机器学习框架NorBERT，应用情感分类模型，对2006年至2022年间从Twitter收集的数据进行分析，以探讨挪威风电反对案例。从68828条带有地理空间信息的推文中，我们展示了2018/2019年间关于风电讨论的加剧，以及直至2020年负面推文增长的趋势，这既体现在区域层面，也体现在挪威整体层面。此外，我们在数据中发现了较弱的地理聚类现象，表明讨论是全国性的，而非由特定区域事件或发展所主导。Twitter数据能够深入洞察公众情绪的时间特性，将本研究扩展到更多技术、国家和数据源（如报纸、其他社交媒体）的案例研究，对于补充传统调查研究并加深对公众情绪的理解可能具有重要意义。