Comparative study of classification approaches for e-mail analysis Online publication date: Thu, 10-Sep-2020
by Pranjal S. Bogawar; K.K. Bhoyar
International Journal of Information and Computer Security (IJICS), Vol. 13, No. 3/4, 2020
Abstract: Illicit messages like threatening and abusive messages affect emotions and psychology of a person. Such messages start exerting influence on mental status, and ultimately physical condition of a person. E-mails are one of the popularly used sources, for communicating personal and official messages. Typically, sentiment analysis of these e-mails includes classifying them into positive, negative and neutral messages. Identifying the sentiments of e-mails using an efficient and effective algorithm is very important and useful step in the domain of e-mail forensics. In this work, support vector machine, k-nearest neighbour, and neural network back-propagation algorithms are used to classify the sentiments of e-mail into positive, negative and neutral categories using self-curated e-mail dataset. This dataset is a combination of Enron's e-mail dataset and publicly available messages converted into e-mails. This paper presents a comparative study of classification approaches for e-mail analysis. Finally, it is concluded that the neural network with the back-propagation training algorithm provides the best results considering the accuracy and the memory requirements with the little compromise on the time required to recognise the sentiment of a given e-mail.
Online publication date: Thu, 10-Sep-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Computer Security (IJICS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org