Collective intelligence of user-generated content (UGC) allows researchers to explore various stakeholders’ perspectives. This study examines the linguistic characteristics, stakeholder networks and collective intelligence shaping solar energy adoption from social media UGC. Using a mixed-method approach of social media analytics and fuzzy-set qualitative comparative analysis (fsQCA), I analyse Twitter data from 2019 to 2021 to investigate identified research questions. I proposed a user adoption motivation-based research model to capture stakeholder-driven collective intelligence. The findings reveal that a combination of technological, social, and economic factors influences sentiment towards solar energy. Positive sentiment emerges from discussions on renewable innovation, energy independence, and environmental responsibility, highlighting users’ trust in the reliability and sustainability of solar solutions. This research substantially enhances the current literature on renewable energy adoption, particularly in social media analytics, by clarifying the intricate dynamics that affect user sentiment and collective intelligence concerning solar energy.