Implementation of Naive Bayes and Support Vector Machine for SMS Spam Classification Using the SMS Spam Collection Dataset
DOI:
https://doi.org/10.54065/artificial.1249Keywords:
SMS Spam Detection, Machine Learning, Text Classification, Naïve Bayes, Support Vector MachineAbstract
Short Message Service (SMS) is one of the most popular communication services on mobile networks. The rapid proliferation of mobile communication has led to an increase in spam messages offering ads, false links, and misinformation, which could pose a threat to user privacy. Automated spam detection using machine learning methods has become a key approach to tackling this problem in recent years. The aim of this research is to apply and train on how the SMS Spam Collection. Dataset for SMS spam classification using 2 machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Several steps are taken, including data preprocessing, text cleanup, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and model training. The performance of the implemented models is assessed using accuracy, precision, recall, F1-score, a confusion matrix, and cross-validation. The results from the experiments show that both algorithms can successfully classify these SMS spam messages. However, the Support Vector Machine model outperforms the Naïve Bayes model, achieving an accuracy of nearly 98% on the classification task. These results demonstrate that machine learning techniques, including Support Vector Machine in combination with TF-IDF feature extraction, provide reliable performance for SMS spam detection, and could be helpful for an automated filter system in m-commerce services.
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