Optimizing Email Spam Detection and Natural Language Processing with Keyword Driven Multi-Class Labeling

Author(s)	Dr. Nilambari Arvind Jagtap, Mr. Om Sanjay Patil, Mr. Sajjan Rajendra Ahiwale, Mr. Ajay Appa Mane
Country	India
Abstract	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.
Keywords	Email SPAM detection, Natural Language Processing (NLP), Machine Learning Algorithms, Data Mining, Deep learning.
Field	Mathematics > Statistics
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-08-24
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7826
Short DOI	https://doi.org/g9x3zs

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International Journal on Science and Technology