• 人工智能与模式识别 •

利用动态多池卷积神经网络的情感分析模型

1. 长沙理工大学 计算机与通信工程学院，长沙 410114
• 出版日期:2018-07-01 发布日期:2018-07-06

Sentiment Analysis with Dynamic Multi-Pooling Convolution Neural Network

YU Tao, LUO Ke

1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
• Online:2018-07-01 Published:2018-07-06

Abstract:

Convolution neural network method based on word vector has achieved very good results in sentiment analysis. However, the semantic word vector learned from the context ignores the word sentiment polarity, and the traditional convolution neural network model does not take into account the sentence structural information. Aiming at these shortcomings, this paper proposes a dynamic multi-pooling convolution neural network sentiment analysis model based on sentiment vector. The skip-gram model and sentiment dictionary are used to train the sentiment word vector, and the dynamic multi-pooling strategy is used to segment the sentence and retain the maximum number of feature values. Compared with machine learning model and traditional convolution neural network model, the experimental results show that the accuracy of dynamic multi-pooling convolution neural network model in sentiment analysis is significantly improved.