Application of improved multiple convolution neural network in emotion polarity classification model

In view of the emotional polarity classification problem, the deep learning has the disadvantages of incomplete information extraction and low precision, a model combining bi-directional gated recurrent unit with multiple convolution neural network is proposed. The unit is used to extract the history and future information of the sentence, then use the multi-convolution neural network for system training, so that the entire model can get more comprehensive information. The model can be training end-to-end, and training for multiple types of text, its adaptability is strong. The experimental results show that the improved model proposed in this paper has a greater improvement than other similar models, and the classification accuracy is also improved significantly.