Contextual Forensic Analysis of Emails Using Machine Learning Algorithms

Emails are essential for personal, professional, and commercial communication, and their increasing volume has led to the need for contextual analysis. Machine learning algorithms offer a more efficient and accurate approach to understanding the meanings, sentiments, and intents behind emails. This paper explores the efficiency of unsupervised machine learning models for text classification in email data, specifically applying K-means and DBSCAN clustering algorithms. The study evaluates the performance using silhouette scores, highlighting the potential of machine learning in enhancing customer feedback, fraud detection, and productivity enhancement in the context of email communication.