An Analysis of Spam Detection using NLP

In the digital era, effective spam detection is essential to ensure secure communication, this paper examines the use of Natural Language Processing (NLP) techniques methods on email and SMS platforms combined with a Recurrent Convolutional Neural Network (RCNN) for spam classification. By integrating RNNs to capture sequential patterns and CNNs to extract local features, RCNN proves to be highly efficient in text classification. The model is trained on email and SMS datasets, leveraging NLP methods like tokenization, stopword removal, and word embeddings to analyze message content. It then classifies messages as either spam or non-spam (ham) with high accuracy and minimal false positives. The experimental results show that the traditional Support Vector Machine (SVM) algorithm achieves an accuracy rate lower than 90%, while the proposed RCNN model demonstrates a notable improvement, reaching an accuracy of 98%. Additionally, the study discusses challenges in spam detection, such as adapting to evolving spam strategies and linguistic variations. The findings demonstrate that the RCNN-based approach enhances spam filtering, ensuring more effective and reliable communication security.