Front-Page News Classification Model Based on the Stacking of Textual Context and Attribute Information

Abstract

The front-page news in authoritative newspapers usually represents extremely significant national policies. Accurately classifying the front-page news from an amount of news helps us to quickly acquire and deeply understand the changing political and economic situations. In this paper, we propose a front-page news classification model StackText based on stacking the textual context and attribute information of news. In the proposed model, we first balance the classification size in the training set through the weighted random sampling algorithm, then construct the context and attribute feature vectors through the textual embedding and statistical analysis of news, and finally pretrain a classifier StackNet based on a neural network to realize the front-page news classification in the testing set. Taking People’s Daily as the experimental example, we compare the proposed model with benchmark methods based on statistical learning and deep learning. Based on the news sets in four stages of People’s Daily, the experimental results of the front-page news classification show that the proposed model achieves the highest average accuracy and a better balance between precision and recall, which is verified by the corresponding $F_1$ -score.