Integrating Sentiment Analysis and Reinforcement Learning for Equitable Disaster Response: A Novel Approach

Efficient disaster response requires dynamic and adaptive resource allocation strategies that account for evolving public needs, real-time sentiment, and sustainability concerns. In this study, a sentiment-driven framework is proposed, integrating reinforcement learning, natural language processing, and gamification to optimize the distribution of resources such as water, food, medical aid, shelter, and electricity during disaster scenarios. The model leverages real-time social media data to capture public sentiment, combines it with geospatial and temporal information, and then trains a reinforcement learning agent to maximize both community satisfaction and equitable resource allocation. The model achieved equity scores of up to $0.5$ and improved satisfaction metrics by $30\%$, which outperforms static allocation baselines. By incorporating a gamified simulation platform, stakeholders can interactively refine policies and address the inherent uncertainties of disaster events. This approach highlights the transformative potential of using advanced artificial intelligence techniques to enhance adaptability, promote sustainability, and foster collaborative decision-making in humanitarian aid efforts.