Reading the climate room through unsupervised analysis of unfiltered climate perspectives

Understanding how competing perspectives shape public perception of climate change is critical, yet existing studies are limited by predefined categories, manual coding, or methodologically constrained topic models. We address this gap by introducing the first large-scale, publicly available dataset of climate discourse, consisting of systematically curated advocate (21,513 documents) and skeptic (26,930 documents) corpora. To facilitate rigorous comparative perspective analysis, we propose an innovative semantic chunking algorithm that segments documents into coherent, semantically meaningful units. Our unsupervised, inductive, exploratory approach reveals distinctive rhetorical strategies: advocates emphasize pragmatic solutions and crisis framings through fear and sadness appeals, while skeptics deploy anti-elite rhetoric and emotional appeals rooted in anger and disgust. These openly accessible resources provide a scalable methodological foundation enabling researchers across psychology, political science, and communication studies to systematically investigate climate discourse dynamics and other contentious societal perspectives.