Spam Detection Using Natural Language Processing (NLP)

Spam detection has become a critical area of research due to the increasing prevalence of unsolicited and malicious emails. Leveraging Natural Language Processing (NLP) for spam detection offers powerful tools for analyzing and classifying email content with high accuracy. This paper explores the evolution of spam detection, highlighting traditional machine learning (ML) approaches and recent advancements like Bidirectional Encoder Representations from Transformers (BERT), AMALS (Alternating Minimum and Least Squares), and feature extraction techniques such as TF- IDF. The integration of NLP techniques enables models to capture contextual and semantic features in text, significantly improving classification performance. Despite these advancements, challenges such as data imbalance, evolving spam strategies, and feature optimization persist. This research discusses current methodologies, evaluates their performance, and provides insights into future directions for building robust spam detection systems.