Deep Learning for Spam Detection Using Neural Networks and NLP

Spam detection is a major challenge in modern digital communication systems due to the rapid growth of fraudulent and unsolicited messages. Traditional machine learning methods rely on manual feature engineering and perform poorly on multilingual and context-dependent text, while deep learning approaches are computationally expensive. This paper presents a hybrid spam detection framework that integrates machine learning and deep learning models using Natural Language Processing (NLP). The system combines Naïve Bayes and Support Vector Machine classifiers with LSTM-based neural networks to improve accuracy and robustness. A fusion strategy is used to combine predictions, reducing false classifications. The model is deployed through an interactive interface for real-time detection and supports multilingual input. Experimental results demonstrate that the proposed hybrid approach outperforms individual models in accuracy and reliability.