融合语法规则的双通道中文情感模型分析

针对使用中文文本进行情感分析时，忽略语法规会降低分类准确率的问题，提出一种融合语法规则的双通道中文情感分类模型CB_Rule。首先设计语法规则提取出情感倾向更加明确的信息，再利用卷积神经网络（CNN）的局部感知特点提取出语义特征；然后考虑到规则处理时可能忽略上下文的问题，使用双向长短时记忆（Bi-LSTM）网络提取包含上下文信息的全局特征，并对局部特征进行融合补充，从而完善CNN模型的情感特征倾向信息；最后将完善后的特征输入到分类器中进行情感倾向判定，完成中文情感模型的构建。在中文电商评论文本数据集上将所提模型与融合语法规则的Bi-LSTM中文情感分类方法R-Bi-LSTM以及融合句法规则和CNN的旅游评论情感分析模型SCNN进行对比，实验结果表明，所提模型在准确率上分别提高了3.7个百分点和0.6个百分点，说明CB_Rule模型具有很好的分类效果。

Analysis of double-channel Chinese sentiment model integrating grammar rules

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.