Application of improved multiple convolution neural network in emotion polarity classification model
Li Rongyu, Feng Zhou, Jing Wang, Xiaojian Yang · 2017
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.