A method to recognize spams based on Dynamic-LSTM
Zhixuan Xiao, Suyao Zhao, Ruiheng Liu, Yixiang Zhang, AngAng Feng, Runjiu Hu · 2022
Nowadays, people are increasingly inseparable from electronic communication tools. Email is one of the important means of communication, but the existence of spams seriously affects the users' usage. This paper focuses on the spam classification problem in a practical context. Real email messages are collected and the classification is performed using the Dynamic_LSTM model. By comparing with algorithms of traditional machine learning as well as ordinary RNN, it is shown that the accuracy of Dynamic_LSTM is increased by 8%.In addition, it is not affected by the max-feature. The experimental results show that the Dynamic_LSTM model performs better at the classification accuracy.