This study examines fluctuations in sentiment and the predominance of topics in discussions linked to climate change on Reddit, utilizing the Reddit Climate Change Dataset. Through the utilization of Natural Language Processing (NLP) tools, such as sentiment analysis and topic modeling, in conjunction with Social Network Analysis (SNA) approaches, we are able to identify notable variations in sentiment among different subreddits and between NSFW and non-NSFW groups. Our data indicates that 43.4% of comments exhibit positive emotion, 46.6% display negative sentiment, and 10% remain neutral. Notably, NSFW subreddits have a greater proportion of positive sentiment. Temporal analysis detects variations in sentiment over time, which may be influenced by external events or policy changes. We employ a range of Social Network Analysis (SNA) principles, including centrality measures and community detection methods, to analyze the patterns of interaction and distribution of sentiment within the network of subreddits. The Louvain algorithm detects 351 discrete communities, showcasing the varied terrain of climate change discussion on Reddit. Topic modeling techniques, such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), uncover significant topics like nuclear energy, fossil fuel consumption, and political interventions. In addition, we employ a content-based recommendation system for subreddits, providing valuable information for enhancing community involvement tactics. The results of our research offer useful insights into the public’s opinions on climate change, highlighting the influence of online communities in shaping discussions. These findings also have important implications for enhancing climate communication methods and informing policy-making.