Spam or Ham? A Hybrid Deep Learning Approach for SMS Spam Detection

With the advent of digital communications, SMS spam has also become a widespread issue, which is inconvenient and even threatening to users. In this project, we advocate a hybrid spam detection model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and TF-IDF, and efficiently leverages deep learning and text-processing methods to identify spam messages. We train our model using the publicly available UCI SMS spam dataset. The interface gives an easy and convenient means of classifying SMS messages. Upon providing a message for classification, the model processes the message very quickly and provides a real-time classification result as either spam or ham. This project introduces a solution for SMS security through a useful spam detection system with excellent user experience. Following the integration of deep learning and conventional text-processing methods, our model has high accuracy and flexibility in spam detection. With this system implemented, we can mitigate risks from spam and ensure digital communication is safer and more reliable.