Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Smart Urban Environments

As smart urban environments become increasingly pivotal in the realm of health crisis management, there is a growing need for sophisticated sentiment analysis algorithms capable of understanding and responding to the dynamic nature of public sentiment during crises. This paper proposes a novel approach that integrates Context-Aware Sentiment Analysis using Reinforcement Learning (CASARL) to improve sentiment analysis's precision as well as flexibility when applied to health emergencies in smart city environments. This approach makes use of deep learning architectures, including Transformer-based models or Long Short-Term Memory (LSTM), to extract intricate contextual information from text data pertaining to illnesses. The algorithm learns optimal sentiment analysis actions through a reward-based system that considers the real-world impact of sentiment classifications during health crises. By taking into consideration the particular circumstances, happenings, and attitudes that are common in smart communities during health emergencies, the suggested method seeks to deal with the shortcomings of conventional sentiment analysis methods. The integration of reinforcement learning ensures adaptability to evolving contexts, allowing the model to dynamically adjust sentiment analysis strategies based on real-time data. The effectiveness of our approach is evaluated through extensive simulations and experiments using historical health crisis data and real-time data from smart urban environments. The findings highlight how the suggested CASARL technique outperforms more conventional sentiment analysis algorithms in terms of accuracy and flexibility, suggesting that it could be a useful instrument for managing health crises in smart urban environments.