Classifying Unwanted Emails using Naïve Bayes Classifier
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Victoria Oluwatoyin Oyekunle, Edward E. Ogheneovo|
|DOI : 10.14445/22312803/IJCTT-V69I9P103|
How to Cite?
Victoria Oluwatoyin Oyekunle, Edward E. Ogheneovo, "Classifying Unwanted Emails using Naïve Bayes Classifier," International Journal of Computer Trends and Technology, vol. 69, no. 9, pp. 12-16, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I9P103
In recent years, the increasing use of Electronic mail for fast and cheap personal, official, academic communication, and electronic commerce has led to the emergence and further widespread of problems caused by unsolicited and unwanted bulk e-mail messages. In this study, the objective is to enhance the classification of incoming e-mails-using the Naïve Bayes classifier-into unwanted and ham (legitimate) based on features in both the Subject text of the email and the Email body. The system segments the input email body into tokens and analyses its structure. The dataset is cleaned, and the total number of unique words are counted and extracted, and then compared with already learned unwanted words in the database. If email is classified as ‘Unwanted with very high degree’ or ‘Unwanted with high degree’, users are notified and advised to block unwanted emails. Some emails were classified as Ham. This means that users can view such messages as legitimate messages.
Electronic mail, Machine Learning, Artificial Intelligence, Spam Filtering, Unwanted Emails, Ham, Phishing, junk Email.
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