Open AccessAutomatic Genre Categorization of Emails into predefined categories using machine learning
Author(s) -
Vinod K. Bhalla
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.2302
Subject(s) - computer science , categorization , classifier (uml) , support vector machine , text categorization , artificial intelligence , machine learning , class (philosophy) , world wide web , information retrieval
In today’s dynamic world, there is a need for fast, efficient, and reliable means of communication. To meet these requirements email system was developed and it got popular with the invention of WWW. Now, the Email system has been used extensively for official, business, and personal communication. On average individual users receive 50-60 mails each day. It is becoming a burden to easily manage emails. So there is a need for effective and reliable means to organize the mails for easy and fast retrieval. An efficient approach is proposed in this paper to classify the mails based on the predefined genres. It has been observed in the proposed research that the classification of emails greatly improves efficiency and saves time and effort to manage them. The results obtained in this paper are very encouraging. Over 90 % of emails are categorized correctly. Email genres are predefined and corresponding keyword lists are generated. Frequency tf-idf of the keywords in the email decides the genre of mail. SVM is used as a multiclass classifier. In this paper need for negative training data has been removed as the proposed classifier works on the principle of one class against the rest.