An AI-Driven Framework For Real-Time Disaster Detection Using Social Media Data With Geo-Spatial And Sentiment Analysis

In recent years, social media platforms have become an important source of real-time information during natural disasters and emergency situations. Millions of users share posts, images, and location information that can provide valuable insights for disaster monitoring and response. However, identifying relevant disaster-related information from the massive volume of social media data remains a significant challenge. This paper presents an AI-driven disaster detection framework that utilizes social media analytics, location intelligence, and sentiment analysis to monitor and identify disaster events in real time. The proposed system collects social media posts and processes them using natural language processing and machine learning techniques to detect disaster-related content. Location intelligence methods are applied to extract geographical information from posts, enabling accurate identification of affected areas. In addition, sentiment analysis is used to evaluate public emotions and urgency levels associated with disaster events. The integrated framework helps emergency response teams gain situational awareness and make timely decisions during critical situations. Experimental evaluation demonstrates that the proposed approach effectively identifies disaster-related posts and provides meaningful insights for disaster management systems. The framework can support authorities and emergency organizations in improving response strategies and enhancing public safety. \\