Forensic Investigation and WhatsApp Chat Analysis Using Named Entity Recognition with Web Based Visualization

WhatsApp, a popular instant messaging app, has emerged as an important source of digital evidence in forensic investigations. This paper presents an automated forensic investigation system that analyzes WhatsApp chat data using Natural language Processing (NLP), specifically Named Entity Recognition (NER) to identify and categorize key entities such as individuals, organizations, locations, and dates. The system reads WhatsApp chat logs and analysis the text including with entity recognition, it implements Machine Learning and Deep Learning Models, such as BiLSTM and LSTM, to train the NER system, enhancing accuracy in recognizing and classifying these entities. The proposed system enhances forensic analysis by providing a web-based visualization interface that dynamically displays the results of NER and communication patterns. By leveraging these techniques, our system efficiently processes unstructured chat logs, extracting meaningful insights while significantly reducing manual effort for investigators. The system is scalable and adaptable to different messaging platforms, making it a valuable tool for law enforcement agencies and forensic analysts. In this study, we demonstrate the system's accuracy, speed, and relevance through multiple case studies on realworld chat datasets.