Geosocial media’s perspective on energy: a text classification approach using natural language processing

This study examines public opinion on various energy sources through Twitter data, focusing on fossil fuels, nuclear energy, and renewable energy sources like solar and wind. Utilizing natural language processing techniques, specifically BERTweet and GPT models, the research analyses tweet categorization based on sentiment and stance related to these energy sources. Our findings reveal a positive shift towards nuclear, solar, and wind energy, contrasting with increasing negative sentiment towards fossil fuels. Notably, BERTweet demonstrates superior precision and recall in tweet categorization compared to GPT-3.5 and GPT-4, which show potential bias against fossil fuels, misclassifying many tweets as opposing them. This study highlights the importance of social media analytics in understanding public opinions and shaping energy policy, suggesting that future research should broaden the scope of data, enhance multilingual capabilities, and improve data visualization to more accurately reflect global public opinion. The results underscore the need for balanced AI training to mitigate bias and more accurately capture diverse perspectives on contentious energy topics. The datasets, code utilized, and interactive maps with word clouds are available at https://doi.org/10.5281/zenodo.15020578 and https://doi.org/10.5281/zenodo.15084294.