A Framework for Spam Toxicity Level Detection using Machine Learning Models
Keywords:
Machine Learning, Spam, Toxicity, Classification.Abstract
Electronic mails are a technique of conveying a message to an individual or group through the internet. When emails first became popular, they could only be used for institutional and scientific research. However, as technology has advanced to the point where it can reach every individual on the planet, everyone now can have an email address in his or her name. As technology advances and more people begin to use email, billions of emails are sent in the name of promotions, ads, and spam. It is challenging to manage emails, and users nowadays are having difficulty locating their essential email. In this work, a framework is proposed for classifying the emails into multiple classes (bills, promotions, personal, spam, and OTP) so that the relevant emails can be identified by the users easily. The framework consists of supervised machine learning (ML) models such as “random forest (RF)”, “support vector machine (SVM)”, “naïve bayes (NB)”, “k-Nearest Neighbour (k-NN)”, and “decision tree (DT)” for classifying mails. A web application was also developed for the same purpose. Results demonstrated that the RF and k-NN classifiers outperformed the other classifiers based on accuracy.
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