Spam email classification based on SVM, Transformer and Naive Bayes

Abstract

As a matter of fact, with the booming of information and big data, there are too many unwanted e-mails called spam sent to peoples e-mail account in recent years. On this basis, it could lead to a lot of problems including occupying the public resources, causing financial loss and so on. With this in mind, spam filtering technique is in need to solve the problem and address the issues. In reality, based on previous analysis, machine learning methods are very effective in spam filtering. On this basis, this study carried out background research of machine learning algorithms in spam filtering, and find the spam e-mail dataset of Kaggle.com, and implement 3 algorithms on the dataset. According to the analysis, Transformer and SVM work better on the dataset, and SVM is the best. At the same time, the current limitations are discussed as well. In addition, the prospects are demonstrated in the meantime.