Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction

Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction

Ayman E. Khedr (Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, Egypt), Amira M. Idrees (Faculty of Computers and Information, Fayoum University, Egypt) and Essam Shaaban (Information Systems Department, Beni-Suef University, Beni Suef, Egypt)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/IJeC.2020040104


One of the current essential methods for communication is electronic email (e-mail). It is currently considered the official method for different business activities such as conducting agreements, the setup of official meetings, and team collaboration. This continuous interest in e-mails as a communication channel has drawn the attention to the need for eliminating spam which have a vital effect on both network resources and business activities. This research focuses on generating a ham-spam lexicon based on text analysis which is aimed to be one of the main resources for detecting personal spam e-mails. The lexicon generation is a key step to efficiently and economically successful spam elimination. The proposed framework has proven its applicability on a dataset of six groups and the classification algorithms have been examined to prove the efficient classification. The research is a step in a wider view for general intelligent business communication and collaboration framework.
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Literature Review

Mining Techniques are currently the key factor for different research in different fields including healthcare systems (Khedr, Kholeif, & Saad, 2017) and education systems (Khedr, Kholeif, & Hessen, 2015). Manipulating these techniques has proved to provide high accuracy in the discovery process such as in (Sultan, Khedr, Idrees, & Kholeif, 2017) which proposed an enhancement in FP-growth associations rules mining algorithm. Other examples are presented in (Khedr, Idrees, & Elseddawy, 2016; Khedr & Borgman, 2006) which proposed a successful enhancement classification mining techniques. Segments of information with different nature are targeted due to the variety of their usage such as in (Khedr & Kok, 2006) for banking data, (Khedr, 2013) for risk management data, and (Khedr, Kholeif, & Hessen, 2015) for learning data. In this section, the research will focus on two consecutive subjects, text analysis with demonstrating its applications and related fields, then a focus on the research for detecting spams from e-mails is discussed.

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