Using convolution control block for Chinese sentiment analysis

Convolutional neural network (CNN) has lately received great attention because of its good performance in the field of computer vision and speech recognition. It has also been widely used in natural language processing. But those methods for English cannot be transplanted due to phrase segmentation. Those for Chinese are not good enough for poorly semantic retrieving. We propose a Chinese sentiment classification model on the concept of convolution control block (CCB). It aims at classifying Chinese sentences into the positive or the negative. CCB based model considers short and long context dependencies. Parallel convolution of different kernel sizes is designed for phrase segmentation, gate convolution for merging and filtering abstract features, and tiering 5 layers of CCBs for word connection in sentence. Our model is evaluated on Million Chinese Hotel Review dataset. Its positive emotion accuracy reaches 92.58%, which outperforms LR_all and DCN by 2.89% and 4.03%, respectively. Model depth and sentence length are positively related to the accuracy. Gate convolution indeed improves model accuracy.