A Method for SMS Spam Message Detection Using Machine Learning

In recent years, it has become increasingly common for individuals to connect with their relatives and friends, read the most news, discuss various social topics by using online platforms such as Twitter Facebook. As a consequence of this, anything that is considered spam can quickly spread among them. The identification widely regarded one significant problems involved in text analysis. Previous studies on detection concentrated primarily English-language content paid little attention other languages. information gathered University California; Irvine served basis development our technology (UCI). this research study, effectiveness supervised machine learning algorithms, J48, KNN, DT, identifying ham communications investigated. SMS becoming more widespread number who use internet continues rise an increasing businesses disclose customers' personal information. E-mail filtering progenitor filtering, which inherits its features. We evaluate proposed method based accuracy, recall, precision. Experiments showed DT obtained higher accuracy compared classifiers.