Abstract: The sentiment analysis task aims to understand and classify the polarity of emotions that people express towards entities and their attributes. In the classification of Chinese text, most of the existing methods have single input feature representation, which makes the models unable to fully learn semantic information. To solve these problems, a dual-channel gated composite network model, named DGCN, is proposed, which uses word vector and character vector as the input of the two channels, which makes up for the defect of word vector caused by the inevitable inaccurate word segmentation and enriches the semantic information. At the same time, the gating mechanism is used to improve the combination mode of channels, so that char vector helps the word vector learn the characteristic information of text better. On each channel, a composite network composed of bidirectional gated recurrent unit network and convolutional neural network is used, so the advantages of the two channels are complementary. The attention mechanism is added to focus on more effective features. The experimental results show that the DGCN model has better accuracy and F1 value in sentiment analysis of Chinese product reviews than the counterparts, and has good application ability.

Key words: sentiment analysis, word vector, character vector, convolutional neural network, bidirectional gated recurrent unit network, gating mechanism ,