Open Access

ARTICLE

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

Feng Xu¹, Xuefen Zhang^2,*, Zhanhong Xin¹, Alan Yang³

1 School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
2 Smart City College, Beijing Union University, Beijing, 100101, China.
3 Amphenol Assemble Tech, Houston, TX 77070, USA.

* Corresponding Author: Xuefen Zhang. Email: .

Computers, Materials & Continua 2019, 58(3), 697-709. https://doi.org/10.32604/cmc.2019.05375

Abstract

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.

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Citations