News Text Classification Method for Edge Computing Based on BiLSTM-Attention

Abstract

In the coming smart city, the explosive growth of data makes the amount of data contained in news texts more and more, which leads to the decrease in the accuracy of traditional machine learning or deep learning models in the news text classification. Therefore, in this paper, we propose a news text classification model based on BiLSTM-Attention. The data set is selected as 30,000 news texts, and the word segmentation is carried out in turn. The stop words are removed, and the word vector is quantified. Then, the data set is cross-validated according to the ratio of training set to validation set of 8:1. Finally, the experiments with the bilstm model, lstm model and bilstm-short text model show that the BiLSTM-Attention model has the highest accuracy and the lowest loss value. In order to further verify the classification performance of BiLSTMAttention model, the experiment is designed again and Bayes and SVM are added to compare. The experimental results show that the accuracy, recall and F1 value of BiLSTM-Attention model are the highest, which proves that BiLSTM-Attention is more suitable for news text classification.