基于一维卷积混合神经网络的文本情感分类

针对情感分类中传统二维卷积模型对特征语义信息的损耗以及时序特征表达能力匮乏的问题,提出了一种基于一维卷积神经网络(CNN)和循环神经网络(RNN)的混合模型。首先,使用一维卷积替换二维卷积以保留更丰富的局部语义特征;再由池化层降维后进入循环神经网络层,整合特征之间的时序关系;最后,经过softmax层实现情感分类。在多个标准英文数据集上的实验结果表明,所提模型在SST和MR数据集上的分类准确率与传统统计方法和端到端深度学习方法相比有1至3个百分点的提升,而对网络各组成部分的分析验证了一维卷积和循环神经网络的引入有助于提升分类准确率。

Text sentiment classification based on 1D convolutional hybrid neural network

Traditional 2D convolutional models suffer from loss of semantic information and lack of sequential feature expression ability in sentiment classification. Aiming at these problems, a hybrid model based on 1D Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) was proposed. Firstly, 2D convolution was replaced by 1D convolution to retain richer local semantic features. Then, a pooling layer was used to reduce data dimension and the output was put into the recurrent neural network layer to extract sequential information between the features. Finally, softmax layer was used to realize the sentiment classification. The experimental results on multiple standard English datasets show that the proposed model has 1-3 percentage points improvement in classification accuracy compared with traditional statistical method and end-to-end deep learning method. Analysis of each component of network verifies the value of introduction of 1D convolution and recurrent neural network for better classification accuracy.