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This study aims to analyse solar power acceptance by different methods in various knowledge domains to gain a holistic view of global, regional, and local acceptance. This includes considering different related aspects of solar energy, including the overall concept, solar panel, the device converting sunlight into electricity, and photovoltaics, the technology. This multidisciplinary approach is possible through the advancement of artificial intelligence technology. Technology acceptance and sentiment, the emotion, are different concepts with slightly different influences on technology deployment. Acceptance can be granted as a social license and can be affected by how the media discusses the technologies. The acceptance further influences investment decisions and wider technology adoption. Sentiment can be obtained by machine or human-made analysis, in which the polarity (positive, negative, or neutral) is defined while the acceptance levels are indicative. This study applies opinion mining, chat generative pre-trained transformer, and generalised aggregated lexical tables methods to analyse the acceptance and sentiment of solar power at different levels. The findings and the original contribution involve highlighting the potential of artificial intelligence to study general acceptance. Artificial intelligence appears capable of providing a fast indication of both media sentiment and the level of acceptance of solar power. Traditional opinion mining seems to be more capable of acknowledging trends. This supports understanding the market environment and factors affecting technology development and deployment. Decision-making can benefit from a fast indication. [Display omitted] • Assessing global, regional, and local acceptance of solar power. • Combining opinion mining, ChatGPT, and CA-GALT to analyse general acceptance. • AI provides a fast indication of both media sentiment and the level of acceptance. • Understanding energy technology acceptance-related chains of reasonings. • Techno-economic context to complement views with causal acceptance models. [ABSTRACT FROM AUTHOR]