Building an Effective Email Spam Classification Model with spaCy
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSpam detectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Privacy-Preserving Spam Filtering using Functional Encryption
Traditional spam classification requires the end-user to reveal the content of its received email to the spam classifier which violates the privacy. Spam classification over encrypted emails enables the classifier to cla…
ClassificationGeneral ClassificationPrivacy PreservingClassification of Spam Emails through Hierarchical Clustering and Supervised Learning
Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on binary classification, spammers bypass them …
Binary ClassificationClassificationClusteringGeneral ClassificationImplementation of Naïve Bayes and Gini Index for Spam Email Classification
Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipien…
feature selectionEffective Email Spam Detection System using Extreme Gradient Boosting
The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to prot…
Spam detectionClassifying spam emails using agglomerative hierarchical clustering and a topic-based approach
Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email p…
Clustering