Optimizing Email Spam Detection and Natural Language Processing with Keyword Driven Multi-Class Labeling
Nilambari Jagtap, Om Patil, Sajjan Ahiwale, Ajay Mane · International Journal on Science and Technology · 2025
Email has become a key part of our daily lives, both at work and at home. It helps us communicate and share information quickly and easily. But with the rise of SPAM emails, there are growing problems like security risks, overloaded inboxes, and missed important messages. SPAM emails often contain scams, viruses, or unwanted ads, which can be dangerous for individuals and businesses. this project explores ways to improve SPAM detection using machine learning and natural language processing (NLP) to better understand email content and reduce errors. We also created a simple keyword-based system that sorts emails into five categories Spam, Promotion, Social, Updates, and Primary. While the keyword system helps manage emails quickly, it can misclassify some messages. the study shows how combining machine learning with NLP can make email filtering smarter and more reliable.