PROACTIVE PUBLIC SAFETY THROUGH DEEP SEMANTIC EVENT ANALYSIS AND NATURAL LANGUAGE UNDERSTANDING

Abstract

In an era of rapid information exchange, ensuring public safety during live events has become a crucial challenge. This study presents a proactive framework for real-time event detection using Deep Semantic Event Analysis and Natural Language Understanding (NLU) to enhance situational awareness and emergency response. The proposed model leverages deep learning architectures— specifically transformer-based NLP models such as BERT and LSTM—to extract contextual meaning and emotional tone from live social media streams, news feeds, and user-generated content. Unlike traditional keyword-based systems, this approach captures semantic relationships, sentiment intensity, and event progression patterns, enabling the early identification of incidents related to crowd panic, violence, accidents, or natural disasters. The system architecture integrates text preprocessing, semantic embedding, and event classification modules, combined with real-time filtering and priority-based alert mechanisms. Through training on large-scale annotated datasets, the model achieves high precision and recall in detecting safety-critical events with minimal false alarms. The results demonstrate the potential of combining deep neural networks and NLP to establish intelligent monitoring systems that can proactively warn authorities and the public. Overall, the proposed framework contributes to next-generation safety management, offering a scalable, adaptive, and explainable solution for real-time event detection in dynamic social environments.