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Detection and Classification of Unwanted Email Contents Using Machine Learning


Author(s):

LIDIYA NIXON , M A COLLEGE OF ENGINEERING,KOTHAMANGALAM; RESHMA RAJAN, M A College of Engineering; SUREKHA MARIAM VARGHESE, M A College of Engineering

Keywords:

Collaberative Filtering, Profanity Filter, Spam Detection, Genetic Algorithm, Machine Learning

Abstract:

Electronic mail, most commonly called email or e-mail since around 1993 is a method of exchanging digital messages from an author to one or more recipients. Email operates across the Internet or other computer networks. Like any other dynamic medium, it is prone to misuse. There is a chance of occurrence of abusive or offensive words in email content. Here a new system is developed to analyze the contents of email body and to list out the offensive content. There are three main phases’ namely content analysis, classification and objectionable content identification. Initially the contents are analyzed and based on this the category of email is identified as social, promotions and general. Then the content is checked to detect the presence of abusive words. If any abusive word is present it is identified and its different possible combinations is predicted using genetic algorithm.


Other Details:

Manuscript Id :IJSTEV3I1146
Published in :Volume : 3, Issue : 1
Publication Date: 01/08/2016
Page(s): 364-367
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