Chinese News Text Classification Based on Convolutional Neural Network

Abstract

With the explosive growth of Internet text information, the task of text classification is more important. As a part of text classification, Chinese news text classification also plays an important role. In public security work, public opinion news classification is an important topic. Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time. This paper introduces a combined-convolutional neural network text classification model based on word2vec and improved TF-IDF: firstly, the word vector is trained through word2vec model, then the weight of each word is calculated by using the improved TF-IDF algorithm based on class frequency variance, and the word vector and weight are combined to construct the text vector representation. Finally, the combined-convolutional neural network is used to train and test the Thucnews data set. The results show that the classification effect of this model is better than the traditional Text-RNN model, the traditional Text-CNN model and word2vec-CNN model. The test accuracy is 97.56%, the accuracy rate is 97%, the recall rate is 97%, and the F1-score is 97%.