AI-Driven Sentiment Analysis for Discovering Climate Change Impacts

Abstract

Highlights: What are the main findings? The analysis uncovered notable trends and patterns in how people discuss climate issues, particularly in relation to regional planning and infrastructure impacts. The integration of AI tools such as entity recognition, sentiment analysis, and interactive visualizations proved effective in making complex climate data more understandable and actionable for policymakers and the public. What is the implication of the main finding? Policymakers and regional planners can use the extracted insights to design more targeted, data-driven strategies for climate adaptation and infrastructure resilience. The approach demonstrates that combining AI and NLP can improve public engagement and awareness by transforming unstructured climate discussions into accessible, actionable information. Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness.