关键词: 卷积神经网络, 双向门限循环单元, 特征融合, 注意力机制, 文本情感分析

Abstract: Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores the context and semantic information of words and loses a lot of feature information in maximal pooling processing, the traditional Recurrent Neural Network (RNN) has information memory loss and vanishing gradient, and both CNN and RNN ignore the importance of words to sentence meaning, a model based on parallel hybrid network and attention mechanism was proposed. First, the text was vectorized with Glove. After that, the CNN and the bidirectional threshold recurrent neural network were respectively used to extract text features with different characteristics through the embedding layer. Then, the features extracted by two networks were fused. And the attention mechanism was introduced to judge the importance of different words to the meaning of sentence. Multiple sets of comparative experiments were performed on the English corpus of IMDB. The experimental results show that the accuracy of the proposed model in text classification reaches 91.46% and F1-Measure reaches 91.36%.

Key words: Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BGRU), feature fusion, attention mechanism, text sentiment analysis