Application of an Improved Convolutional Neural Network Algorithm in Text Classification

Abstract

This paper proposes a text classification model based on a combination of a convolutional neural network (CNN) and a support vector machine (SVM) using Amazon review polarity, TREC, and Kaggle as experimental data. By adding an attention mechanism to simplify the parameters and using the classifier based on SVM to replace the Softmax layer, the extraction effect of feature words is improved and the problem of weak generalization ability of the CNN model is solved. Simulation experiments show that the proposed algorithm performs better in precision rate, recall rate, F1 value, and training time compared with CNN, RNN, BERT and term frequency-inverse document frequency (TF-IDF).