Real Time Disaster Information Aggregation Model

Disaster management and response rely heavily on real-time and accurate information. Previous research has focused on collecting disaster data from official government reports and structured news sources. While these methods provide reliable information, they often suffer from delays and lack immediate public sentiment analysis. In our project, we enhance disaster response by extracting data from Reddit and Google News, allowing access to both real-time information and public sentiment. Using web scraping and Natural Language Processing (NLP) techniques, we filter relevant disaster-related posts and news articles. Sentiment analysis is performed to assess the emotional tone of public reactions. Additionally, by applying classification models, we categorize the severity of the events, providing authorities with crucial insights. This integrated system offers a more immediate, diverse, and sentiment-aware disaster information pipeline compared to traditional methods, aiming to improve the speed and efficiency of disaster management efforts.