A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning

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 fromTransformers (BERT). The model demonstrates high effectiveness across various communication platforms, including emails, SMS, and social media, achieving an accuracy exceeding 98.5%.