Text sentiment classification method based on DPCNN and BiLSTM

Abstract

In recent years, deep learning network models have been widely used in the aspect of text emotion classification and have achieved remarkable achievements. The traditional TextCNN network can only extract local spatial features of sentences, while the improved DPCNN model has the ability to capture long-distance dependence of the text by deepening the network depth. At the same time, bi-LSTM model is characterized by learning temporal information of text. Therefore, this paper combines the two models, which can not only obtain the spatial local information of the text, but also further strengthen the ability to understand and learn the semantic association information of the text. Experimental results show that the classification effect of the model used in this paper is better than the single model.