SMS Spam Detection - T. Sowmya Krishna, P. Jyothsna, P. Pravallika T. Pavani (Mathematical Statistician and Engineering Applications, 2022)
The number of people who use mobile devices is on the rise. SMS, or short message service, is a text messaging service that is available on both basic phones and smartphones. As a result, SMS traffic dramatically increased. The number of spam messages also went up. Junk or unwanted messages, also known as spam, can be used to harm users by wasting their time and stealing valuable information. Spammers attempt to send spam messages for financial or business reasons, such as market growth, information about lottery tickets, credit card information, fraudulent messages or advertisements, etc. Effective spam detection is a crucial method for determining whether an SMS is spam or not. Special attention should be paid to spam classification. For SMS spam detection, we used a variety of machine learning methods in this paper. Using our dataset, we develop a model for detecting spam. The Multinomial Nave Bayes model outperforms previous models in spam detection with an accuracy of 96.8%, as demonstrated by our experiments. For all of our implementations, we used Python