Predictive Sentimental Analysis of Spam Detection using Machine Learning

Abstract

The development of technology in recent years, a surge in the marketing content and the inexpensive choice of sending text messages for promotional and other advertising purposes has made the practice of SMS (Short Message Service) on cell phones escalate to such a prominent manner that cellphones are constantly overburdened through spam SMS. As a result, important messages like bank or work-related information can get lost among the unimportant spam messages. Moreover, these spam messages are extremely harmful since they can breach our privacy and expose our personal information to hackers and other potentially hazardous sources. This issue can be mitigated by employing the Sentiment Analysis and variety of Machine Learning Algorithms that are appropriate for separating spam from important communication. This paper analyses the methodology of intelligent spam filtering approaches in the SMS paradigm with respect to mobile text message spam. It tests some of the most prominent spam filtering algorithms on publicly available SMS spam datasets to discover which ones perform best in this situation.