EXPLAINABLE DEEP NLP FRAMEWORK FOR PUBLIC SAFETY EVENT DETECTION AND RISK ASSESSMENT

Abstract

This paper employs state-of-the-art natural language processing (NLP) to analyze substantial quantities of unstructured text data from emergency communication logs, news, and social media. Public safety incidents are identified in this analysis. In real time, safety events can be identified and categorized using machine learning models such as feature extraction, preprocessing, and deep learning architectures. Accidents, crimes, and natural disasters are all safety-related events. Sentiment analysis, named entity recognition, and topic modeling are implemented by the system to enhance the situational awareness and early warning capabilities of law enforcement and first responders. According to experiments, monitoring systems that are driven by natural language processing (NLP) are more responsive, accurate, and precise than conventional systems. This demonstrates their significance in the proactive management of public safety.