Spam Message Filtering with LSTM: A Deep Learning-Based Text Classification Approach
DOI:
https://doi.org/10.48047/wn7t1e87Keywords:
Spam Detection, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Natural Language Processing (NLP), SMS Classification, Text Preprocessing, Deep Learning, Message Filtering, Sequence Modeling.Abstract
With the exponential growth of digital communication, spam messages have become a persistent threat, disrupting user
experience and posing serious security risks. Traditional rule-based and shallow machine learning approaches often fall short in
accurately detecting evolving spam patterns. This study presents a robust spam classification framework leveraging deep learning,
specifically Long Short-Term Memory (LSTM) networks and a hybrid CNN-GRU architecture, to classify SMS messages as spam or
legitimate (ham). The system employs comprehensive preprocessing
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