Research Article
A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning
@ARTICLE{10.4108/airo.8309, author={Mohini Tyagi and Pradeep Kumar Singh and Shivam Kumar Yadav and Sanjay Kumar Soni}, title={A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={4}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2025}, month={3}, keywords={Bidirectional Encoder Representations from Transformers, BERT, Machine Learning, ML, Natural Language Processing, NLP, Spam/Ham, Support Vector Machine, SVM}, doi={10.4108/airo.8309} }- Mohini Tyagi
Pradeep Kumar Singh
Shivam Kumar Yadav
Sanjay Kumar Soni
Year: 2025
A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning
AIRO
EAI
DOI: 10.4108/airo.8309
Abstract
As digital communication rapidly expands, the issue of unsolicited and unwanted messages, commonly known as spam, has become a major concern. This paper introduces an advanced spam detection system that integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system differentiates between spam and legitimate messages by employing a hybrid model that combines Naive Bayes, Support Vector Machines (SVM), and deep learning models like Bidirectional Encoder Representations from Transformers (BERT). The model demonstrates high effectiveness across various communication platforms, including emails, SMS, and social media, achieving an accuracy exceeding 98.5%.
Copyright © 2025 M. Tyagi et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
