  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.
