关键词: 情感分析, 深度学习, 情感词向量, 卷积神经网络, 动态多池

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.

Key words: sentiment analysis, deep learning, sentiment word vector, convolution neural network, dynamic multi-pooling