Authors: Thogaru Aahalya, P.S.V Krishna
Abstract: The widespread adoption of social media platforms has transformed the way disaster-related information is generated and disseminated, providing valuable real-time insights during emergency situations. However, extracting reliable and actionable information from the enormous volume of unstructured social media content remains a significant challenge due to data noise, misinformation, and incomplete contextual information. This paper presents an intelligent disaster monitoring framework that integrates artificial intelligence, natural language processing, sentiment analysis, and location intelligence to enable real-time detection of disaster events from social media streams. The proposed framework systematically collects and preprocesses user-generated content, identifies disaster-related posts through machine learning techniques, extracts geographic information to determine affected regions, and evaluates public sentiment to assess the severity and urgency of ongoing incidents. By combining textual analysis with geospatial intelligence, the system enhances situational awareness and provides timely decision support for emergency response agencies. Experimental evaluation demonstrates that the proposed framework achieves reliable disaster detection performance while improving the accuracy of event localization and public sentiment assessment. The proposed approach offers a scalable and intelligent solution for disaster management, facilitating faster emergency response, effective resource allocation, and improved public safety during crisis situations.
DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.152
