A Comprehensive Analysis of Text Analysis Algorithms for E-mail Itemization

Electronic mail or E-mail is the message in a formatted manner that is used for personal or official communication. Communication through email is preferable because it is quick, easy, and requires an only internet connection which makes it accessible from anywhere in the world. As email is a quicker way of communication so the proportion of usage increases gradually which leads to an increase in the demand for unmanned governance. Automatic management is required for spam categorization, phishing of email, multiple folder arrangement, etc. The spam message quantity has increased by 30 percent in the past year across different services. An average person receives 40–50 emails per day out of which 55 percent are identified as spam. These days spammers use numerous techniques to fulfill their ill intentions through fraudulent activities. So there is a requirement for email text summarization and analysis. The backbone of this paper is a comprehensive study of existing techniques for spam mail categorization using various machine learning algorithms. Further, the aim of this paper is to build a robust method to categorize these spam and ham mail.