Text Sentiment Analysis based on Parallel TCN Model and Attention Model

Abstract

Aiming at the problem that the traditional single convolutional neural network cannot completely extract comprehensive text features, this paper proposes a text sentiment classification based on the parallel TCN model of attention mechanism. First, obtain the comprehensive text features with the help of parallel Temporal Convolutional Network (TCN). Secondly, in the feature fusion layer, the features obtained by the parallel TCN are fused. Finally, it combines the attention mechanism to extract important feature information and improve the optimized text sentiment classification effect. And conducted multiple sets of comparative experiments on the two sets of Chinese data sets, the accuracy of the model in this paper reached 92.06% and 92.71%. Proved that the proposed model is better than the traditional single convolutional neural network.