SMS Spam Detection Using Machine Learning

SMS Spam Detection Using Machine Learning

Dr.M.Sengaliappan1, C. Deepan Raj2

1Head of the Department, Department of Computer Applications,

Nehru College of management, Coimbatore, Tamilnadu, India

ncmdrsengaliappan@nehrucolleges.com

2Student of II MCA, Department of Computer Applications

Nehru College of management, Coimbatore, Tamilnadu, India

deepanraj0314@gmail.com

Abstract: SMS spam detection is a crucial problem in mobile communication, as unsolicited and potentially harmful messages can lead to privacy invasion, fraud, and disruption of user experience. This study presents an effective approach to identifying spam SMS using machine learning techniques. By leveraging natural language processing (NLP) methods to extract features from text messages, we develop a predictive model that classifies SMS into spam or legitimate categories. The dataset used in this research consists of a large corpus of labelled SMS messages. Various preprocessing steps, such as tokenization, stop word removal, and stemming, are applied to convert raw text into meaningful features. These features are then used to train machine learning models, including Naive Bayes, Support Vector Machines (SVM), Random Forest and like Long Short-Term Memory (LSTM).

Keywords: SVM, Naïve Bayes,  Random Forest, Clasification modules, Long Short-Term Memory (LSTM).