Climate change's impact on human health poses unprecedented and diverse challenges. Unless proactive measures based on solid evidence are implemented, these threats will likely escalate and continue to endanger human well-being. The escalating advancements in information and communication technologies have facilitated the widespread availability and utilization of social media platforms. Individuals utilize platforms such as Twitter and Facebook to express their opinions, thoughts, and critiques on diverse subjects, encompassing the pressing issue of climate change. The proliferation of climate change-related content on social media necessitates comprehensive analysis to glean meaningful insights. This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. We use ClimateBERT, a pretrained model fine-tuned specifically for the climate change domain. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change. Analyzing tweet sentiments allows a deeper comprehension of public perceptions, concerns, and emotions about this critical global challenge. The findings from this experiment unearth valuable insights into public sentiment and the entities associated with climate change discourse. Policymakers, researchers, and organizations can leverage such analyses to understand public perceptions, identify influential actors, and devise informed strategies to address climate change challenges.

翻译：气候变化对人类健康的影响带来了前所未有的多样化挑战。除非基于可靠证据采取积极措施，否则这些威胁可能会持续升级并危害人类福祉。信息与通信技术的持续进步促进了社交媒体平台的广泛普及与应用。人们利用Twitter和Facebook等平台就包括气候变化这一紧迫议题在内的各类主题发表观点、见解与评论。社交媒体上气候变化相关内容的激增需要开展全面分析以提取有价值的见解。本文运用自然语言处理技术分析气候变化话语，量化气候变化相关推文的情感倾向。我们采用ClimateBERT这一专为气候变化领域微调的预训练模型进行研究，旨在识别个体表达的情感并揭示公众对气候变化看法的模式。通过分析推文情感，可以更深入理解公众对这一重大全球挑战的认知、关切与情绪。实验发现揭示了公众情感及气候变化话语相关实体的重要洞见。政策制定者、研究者和组织可借助此类分析理解公众认知、识别关键影响者，并制定应对气候变化挑战的策略方案。