Email Prioritization Using Machine Learning

Personal and business users prefer to use e-mail 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 mails received on the same day is becoming difficult for any users, as the volume of critical e-mails continues to grow, the need to automate the management of e-mails 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 user need to look these mails. Main area of classification is the important mail folder and categorizing it into different classification by using Naïve Bayes algorithm. By Giving the weights to words present in mails we can find out the total weight of that mails and by comparing the weights with the other ones we can prioritize the mails so that it will be efficient to 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.

Keywords: Email Classification, Machine Learning Techniques, Multi-Folder Classification

Suggested Citation: Suggested Citation

Choudhari, Swapnil and Choudhary, Narayan and Kaware, Sumit and Shaikh, Ahmed, Email Prioritization Using Machine Learning (April 4, 2020). Available at SSRN: https://ssrn.com/abstract=3568518 or http://dx.doi.org/10.2139/ssrn.3568518