Alan Zheng¹ and Carlos Gonzalez², ¹USA, ²California State Polytechnic University, USA

Climate change is an urgent global issue, with natural disasters becoming more severe and frequent due to human activities. Understanding public sentiment around these events can inform climate awareness and policy. We developed Climate Change Pulse, a web-based tool that visualizes natural disasters alongside Twitter data to analyze how proximity and time influence climate-related sentiments. Using the Climate Change Twitter Dataset, we examined over 15 million tweets, mapping them with disaster data through an interactive UI. Challenges included missing geospatial data and sentiment classification limitations, addressed by refining data filters and leveraging embedded tweets. Our experiments tested how distance and time around disasters affect sentiment, revealing that proximity intensifies negative emotions, and climate change deniers exhibit surprisingly strong negative sentiments. Compared to prior methodologies focused on data collection or basic sentiment analysis, our approach emphasizes user interactivity and behavioral analysis. ClimateChangePulse offers a dynamic way to understand climate discourse, bridging data insights with public engagement.

Natural Language Processing, Machine Learning, Climate Change, Disasters, Sentiment