Building an Effective Email Spam Classification Model with spaCy

• URL: http://arxiv.org/abs/2303.08792v1
• Date: Wed, 15 Mar 2023 17:41:11 GMT
• Title: Building an Effective Email Spam Classification Model with spaCy
• Authors: Kazem Taghandiki
• Abstract summary: Author has used spaCy natural language processing library and 3 machine learning (ML) algorithms Naive Bayes (NB), Decision Tree C45 and Multilayer Perceptron (MLP) in Python programming language to detect spam emails collected from Gmail service.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today, people use email services such as Gmail, Outlook, AOL Mail, etc. to communicate with each other as quickly as possible to send information and official letters. Spam or junk mail is a major challenge to this type of communication, usually sent by botnets with the aim of advertising, harming and stealing information in bulk to different people. Receiving unwanted spam emails on a daily basis fills up the inbox folder. Therefore, spam detection is a fundamental challenge, so far many works have been done to detect spam using clustering and text categorisation methods. In this article, the author has used the spaCy natural language processing library and 3 machine learning (ML) algorithms Naive Bayes (NB), Decision Tree C45 and Multilayer Perceptron (MLP) in the Python programming language to detect spam emails collected from the Gmail service. Observations show the accuracy rate (96%) of the Multilayer Perceptron (MLP) algorithm in spam detection.

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