计算机工程与应用2017，Vol.53Issue(22)：111-115,5.DOI:10.3778/j.issn.1002-8331.1605-0295
基于改进的卷积神经网络的中文情感分类
Chinese text sentiment classification based on improved convolutional neural networks
张绮琦 1张树群 1雷兆宜1
作者信息
- 1. 暨南大学 信息科学技术学院,广州 510632
- 折叠
摘要
Abstract
A method of sentiment classification based on convolutional neural networks for Chinese comments, which is expressed by pre-train word vectors, is presented. Classic convolutional neural networks is stacked by convolutional layers, pooling layers and fully connected layer. An improved convolutional neural networks in which a cascade cross channel convolutional layer replaces the traditional linear convolutional filter is proposed to improve and enhance the generalization of the network. The experimental results show that the improved convolutional neural networks achieves better perfor-mance with the recognition rate of 91.89%and an acceptable training speed, superior to basic convolutional neural networks.关键词
情感分类/深度学习/词向量/卷积神经网络Key words
sentiment classification/deep learning/word embedding/convolutional neural networks分类
信息技术与安全科学引用本文复制引用
张绮琦,张树群,雷兆宜..基于改进的卷积神经网络的中文情感分类[J].计算机工程与应用,2017,53(22):111-115,5.
