Natural Language Processing for Climate Change Policy Analysis and Public Sentiment Prediction: A Data-Driven Approach to Sustainable Decision-Making

• Author(s): Jessica Obianuju Ojadi ; Ekene Cynthia Onukwulu ; Chinekwu Somtochukwu Odionu ; Olumide Akindele Owulade
• Paper ID: 1705008
• Page: 731-751
• Published Date: 30-09-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 3 September-2023

Abstract

Climate change policy decisions require comprehensive analysis of regulatory frameworks and public sentiment to ensure effective and sustainable solutions. Natural Language Processing (NLP) has emerged as a powerful tool for analyzing climate policies and predicting public sentiment, leveraging vast textual data sources such as government reports, social media, and news articles. This explores the role of NLP in extracting insights from climate policy documents, identifying key themes, and tracking policy evolution over time. Additionally, NLP-driven sentiment analysis provides a real-time understanding of public attitudes toward climate change, enabling policymakers to align regulations with societal concerns. By employing techniques such as topic modeling, sentiment classification, and named entity recognition, NLP facilitates the systematic evaluation of climate policies, helping identify gaps, inconsistencies, and emerging trends. Machine learning models, including transformer-based architectures like BERT and GPT, enhance the accuracy of policy analysis and sentiment prediction, offering deeper insights into the discourse surrounding climate action. Moreover, NLP-driven approaches help detect misinformation, assess the impact of policy communication, and predict potential public reactions to new regulations. Despite its advantages, NLP in climate policy analysis faces challenges such as data bias, ethical considerations, and computational complexities. Addressing these limitations requires robust data preprocessing techniques, interdisciplinary collaboration, and improved AI transparency. This paper highlights real-world applications of NLP in climate governance, including sentiment analysis of climate-related social media discussions and predictive modeling of policy impact. By integrating NLP with climate change decision-making, policymakers can leverage data-driven insights to craft effective, evidence-based policies. The study underscores the transformative potential of NLP in enhancing sustainable governance and fostering public trust in climate initiatives. Future research should focus on improving model interpretability and expanding multilingual capabilities to ensure inclusive climate policy analysis.

Keywords

Natural language processing, Climate change, Policy analysis, public sentiment prediction, Data-driven, Review

Citations

IRE Journals:
Jessica Obianuju Ojadi , Ekene Cynthia Onukwulu , Chinekwu Somtochukwu Odionu , Olumide Akindele Owulade "Natural Language Processing for Climate Change Policy Analysis and Public Sentiment Prediction: A Data-Driven Approach to Sustainable Decision-Making" Iconic Research And Engineering Journals Volume 7 Issue 3 2023 Page 731-751

IEEE:
Jessica Obianuju Ojadi , Ekene Cynthia Onukwulu , Chinekwu Somtochukwu Odionu , Olumide Akindele Owulade "Natural Language Processing for Climate Change Policy Analysis and Public Sentiment Prediction: A Data-Driven Approach to Sustainable Decision-Making" Iconic Research And Engineering Journals, 7(3)