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Sentiment analysis of solar energy in U.S. Cities: a 10-year analysis using transformer-based deep learning

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  • Serena Y. Kim

    (North Carolina State University)

  • Crystal Soderman

    (University of Colorado Denver)

  • Lan Sang

    (University of Colorado Boulder)

Abstract

This study examines U.S. public sentiment toward solar energy from 2013 to 2022 by analyzing 8 million social media posts using RoBERTa, a transformer-based deep learning algorithm. While sentiment has been generally positive, it has declined since 2016, driven by increasing negativity. The analysis reveals significant and widening regional disparities, with Republican-leaning and Southern U.S. regions showing more pronounced negativity. A two-way fixed-effects panel analysis at the municipality level indicates that positive sentiment is associated with high solar radiation and a larger remote-working population, while negative sentiment is linked to a greater prevalence of multifamily housing. These results highlight the complex interplay of environmental, socioeconomic, political, and technological factors in shaping solar energy sentiment and underscore the need for tailored, region-specific strategies. The computational approach, which combines natural language processing and geospatial analysis of social media data, provides a scalable framework for sentiment analysis across diverse topics and regions, extending beyond the specific case of solar energy in the United States.

Suggested Citation

  • Serena Y. Kim & Crystal Soderman & Lan Sang, 2025. "Sentiment analysis of solar energy in U.S. Cities: a 10-year analysis using transformer-based deep learning," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-26, May.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00365-z
    DOI: 10.1007/s42001-025-00365-z
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