Spam SMS Classification Using Machine Learning

Abstract

Over the past few years, the use of emails and text messages has drastically increased. Short Message Service (SMS) on cellphone providers and related apps, like Whatsapp, is one of the best and fastest ways to communicate among users. SMSs are used and sent globally for personal and business purposes. However, alongside safe SMSs, the users may receive fraudulent Spam SMSs, which could cause security issues and inconvenient for the users. Numerous Spam messages are being sent daily for both personal and professional benefits. Accurately identifying Spam SMS is a challenge. The objective of this research is to build a model utilizing machine learning and deep learning to understand the semantics of SMSs and classify them to either Spam or non-Spam (Ham). We used a pre-trained BERT model and combined it with several machine learning and deep learning models. The results indicated that BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracies respectively on the test dataset.