Personalized E-mail Sapm Filtering using Support Vector Machine

Abstract

Communication through e-mails remains to be highly formalized, official and essential method for the exchange of information even after increasing use of social networking applications. With the evolution of technology and globalization, the need for official and personal communication has greatly increased. The advanced transformation of communication medium has enabled the users to promote their associations at both the professional as well as at the personal level. Electronic mail, commonly referred to as e-mail has become one the most widely used communication facilities due to its usage convenience, quick delivery, low cost, and no constraints of time and location. Though e-mails are considered to be the most reliable medium of communication, a huge number of unsolicited e-mails are delivered to Internet users every day without any personal or commercial level of interest. The inevitable downside of e-mail service is a continuously growing ratio of unwanted and useless e-mails called spam e-mails. An ever-increasing ratio of spam e-mails raises a major issue with two different perspectives; the unsolicited content and the individual user's consent. The content of spam e-mails changes over a time due to the adversarial nature of spam e-mails. The content of legitimate e-mails is influenced by the communication pattern of an individual user. So, the content of an e-mail, being the most discriminating measure, e-mail filtering is a prominent application of content-based binary text classification problems. The discrimination of an e-mail as spam of legitimate highly depends on individual user's consent. The user consent is generally influenced by his personal.