Mobile Phone Spam Text Classification Based on Prior Knowledge

Abstract

Due to the rapid development of the Internet, the data generated by people in life is growing at an exponential rate, and Short Message Service (SMS) data is one of the social media products of mobile phone users. The main discussion direction of this article is how to distinguish spam messages and obtain effective information from them, and distinguish the information expressed by the messages themselves. In recent years, Deep learning has made great breakthroughs in the field of textual classification of natural language processing, so this paper will do more research and breakthrough on spam texts using the deep learning method. This article will introduce a Model (RCM) combined with a priori information extracted by Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Firstly, obtain the temporal and spatial information characteristics of the sentence using the bidirectional LSTM as the prior information of the information itself before reading the information, and then merge with the feature information processed by the CNN to improve the accuracy of the spam prediction. Compared with the previous model, there is some innovation in getting the priori information extracted by LSTM, and it is also improve the performance standard.