Email Prioritization Using Machine Learning (original) (raw)

Personal and business users prefer to use email as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. Finding out the important mails of the all the emails received on the same day is becoming difficult for many users, as the volume of critical emails continues to grow, the need to automate the management of emails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. To achieve the objective of study, analysis and comprehensive review to explore the classification as per the importance of emails as users need to look at these mails. The main area of classification is the primary inbox which contains some of the very important mails so to prioritize these mails we are using Natural Language Processing, here we are removing the stop-words present in it and assigning weights to the remaining words. By using frequency count, weight, and access time we can prioritize the mails so that it will be efficient for users to look for the important mails only. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.