利用spaCy构建有效的垃圾邮件分类模型
Building an Effective Email Spam Classification Model with spaCy
摘要：
如今，人们使用Gmail、Outlook、AOL Mail等电子邮件服务，以最快的速度相互沟通，发送信息和公函。垃圾邮件或垃圾邮件是对这种类型的通信的主要挑战，通常由僵尸网络发送，目的是向不同的人批量发送广告，伤害和窃取信息。每天收到不需要的垃圾邮件会填满收件箱文件夹。因此，垃圾邮件检测是一个根本的挑战，到目前为止，许多工作已经做了使用聚类和文本分类方法来检测垃圾邮件。在本文中，作者使用Python程序设计语言中的spaCy自然语言处理库和3种机器学习（ML）算法Naive Bayes（NB）、决策树C45和Multilayer Perceptron（MLP）来检测从Gmail服务收集的垃圾邮件。实验结果表明，多层感知器（MLP）算法在垃圾邮件检测中的准确率为96%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
