文献类型：期刊文献

英文题名：Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

作者：Xu, Feng[1];Zhang, Xuefen[2];Xin, Zhanhong[1];Yang, Alan[3]

第一作者：Xu, Feng

通讯作者：Zhang, XF[1]

机构：[1]Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China;[2]Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China;[3]Amphenol Assemble Tech, Houston, TX 77070 USA

第一机构：Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China.|[1141733]北京联合大学继续教育学院;[11417]北京联合大学;

年份：2019

卷号：58

期号：3

起止页码：697-709

外文期刊名：CMC-COMPUTERS MATERIALS & CONTINUA

收录：;EI(收录号：20191806852070);Scopus(收录号：2-s2.0-85064818432);WOS:【SCI-EXPANDED(收录号:WOS:000460298000008)】；

语种：英文

外文关键词：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 monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In 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 accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment 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.