Bayesian Classification for SMS Spam Detection in Mobile Devices

Abstract

Abstract: The abundance of unwanted spam messages complicates the use of Short Message Service (SMS) for efficient communication in modern times. This study investigates developing and utilizing a Naive Bayes Theorem-based Ham/Spam detection system. Because of its ease of use and effectiveness in text classification tasks, the Naive Bayes classifier is used. A collection of SMS messages labeled as” spam” or” ham” (non-spam) makes up the dataset that was used for testing and training. Preprocessing methods, including tokenization, stop-word elimination, and stemming, are employed to extract pertinent features from the text messages. The Naive Bayes classifier learns how words relate to whether they’re in a spam or non-spam message by looking at some examples from the dataset. Utilizing criteria such as accuracy, precision, and confusion matrix on a separate testing set, the classifier’s performance is evaluated. Additionally, the impact of varying parameters such as smoothing techniques and feature selection methods on the classifier’s performance is analyzed. The experimental results used to distinguishing between ham and spam messages in SMS communication