Detection of SMS Spam Messages Using TF-IDF Vectorizer and Deep Learning Models
Joel C. De Goma, John Adam Bravo, Springtime Prudente, Robert Francis Rondilla · 2024
The spread of SMS spam messages has grown as a major issue, offering a chronic annoyance to mobile phone users worldwide. These unwanted SMS, sent with malevolent purpose or to promote a company, have evolved into increasingly complex schemes. In response to this expanding threat, this article sets out to improve existing SMS spam detection models using deep learning methodology and word embedding techniques. The researchers will emphasize the importance of combating SMS spam and the history of spam filtering, as well as detail the study technique, which includes data pretreatment such as punctuation removal, lowercase conversion, word stemming, and TF-IDF vectorization. The experimental framework employs both LSTM and BiLSTM models, both with and without TF-IDF vectors, for a total of four unique models. Each model is subjected to rigorous K-Fold cross-validation, with the results comparing the efficacy of TF-IDF vectorization in boosting SMS spam detection. This study aims to provide mobile phone users with improved defenses against the harmful threat of SMS spam.