Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

作     者：Feng Xu Xuefen Zhang Zhanhong Xin Alan Yang 

作者机构：School of Economics and ManagementBeijing University of Posts and TelecommunicationsBeijing100876China Smart City CollegeBeijing Union UniversityBeijing100101China Amphenol Assemble TechHoustonTX 77070USA. 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第58卷第3期

页      面：697-709页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：Convolutional neural network(CNN) deep learning learning rate normalization sentiment analysis. 

摘      要：Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web *** sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring *** accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass *** learning is a hot research topic of the artificial intelligence in the recent *** now,several research groups have studied the sentiment analysis of English texts using deep learning *** contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this *** this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis *** feature values of the CNN after the training process are nonuniformly *** order to overcome this problem,a method for normalizing the feature values is ***,the dimensions of the text features are optimized through ***,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better *** results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.