Concerning the problem that ignoring the grammar rules reduces the accuracy of classification when using Chinese text to perform sentiment analysis, a double-channel Chinese sentiment classification model integrating grammar rules was proposed, namely CB_Rule (grammar Rules of CNN and Bi-LSTM). First, the grammar rules were designed to extract information with more explicit sentiment tendencies, and the semantic features were extracted by using the local perception feature of Convolutional Neural Network (CNN). After that, considering the problem of possible ignorance of the context when processing rules, Bi-directional Long Short-Term Memory (Bi-LSTM) network was used to extract the global features containing contextual information, and the local features were fused and supplemented, so that the sentimental feature tendency information of CNN model was improved. Finally, the improved features were input into the classifier to perform the sentiment tendency judgment, and the Chinese sentiment model was constructed. The proposed model was compared with R-Bi-LSTM (Bi-LSTM for Chinese sentiment analysis combined with grammar Rules) and SCNN model (a travel review sentiment analysis model that combines Syntactic rules and CNN) on the Chinese e-commerce review text dataset. Experimental results show that the accuracy of the proposed model is increased by 3.7 percentage points and 0.6 percentage points respectively, indicating that the proposed CB_Rule model has a good classification effect.

For the loss of much semantic information in dimension reduction of text features when using pooling layer of the traditional Convolutional Network (CNN) model, a Convolutional Neural Network model based on Singular Value Decomposition algorithm (SVD-CNN) was proposed. Firstly, an improved Active Learning algorithm based on Density Center point sampling (DC-AL) was used to tag samples contributing a lot to the classification model, obtaining a high-quality model training set at a low tagging cost. Secondly, an SVD-CNN barrage text classification model was established by combining SVD algorithm, and SVD was used to replace the traditional CNN model pooling layer for feature extraction and dimension reduction, then the barrage text classification task was completed on these bases. Finally, the model parameters were optimized by using Partial Sampling Gradient Descent algorithm (PSGD). In order to verify the effectiveness of the improved algorithm, multiple barrage data sample sets were used in the comparison experiments between the proposed model and the common text classification model. The experimental results show that the improved algorithm can better preserve semantic features of the text, ensure the stability of training process and improve the convergence speed of the model. In summary, the proposed algorithm has better classification performance than traditional algorithms on multiple barrage texts.