What question did this study set out to answer?

The aim is to assess various machine learning models for their effectiveness in detecting SMS spam.

January 18, 2026Open Access

Optimizing SMS Spam Detection Using Machine Learning Models

Key Points

The aim is to assess various machine learning models for their effectiveness in detecting SMS spam.
Evaluated multiple machine learning models using the SMS Spam Collection dataset.
Preprocessed dataset with text cleaning techniques and TF-IDF vectorization.
Trained and evaluated models including Naïve Bayes, Logistic Regression, Random Forest, and SVM using metrics like accuracy and F1-score.
SVM model achieved the highest accuracy of 98%, outperforming other models.
Demonstrated significant effectiveness in classifying SMS as spam or not.

Abstract

Spam detection is an important problem in natural language processing. In this study, multiple machine learning models are evaluated for SMS spam detection using the SMS Spam Collection dataset. The dataset is preprocessed using text cleaning techniques and TF-IDF vectorization. Several models including Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM) are trained and evaluated using accuracy, precision, recall, and F1-score. Experimental results show that SVM achieves the highest accuracy of 98%, making it the most effective model for spam detection in this work.

Optimizing SMS Spam Detection Using Machine Learning Models

Key Points

Abstract

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