基于双通道卷积神经网络的文本情感分类算法

doi:10.16451/j.cnki.issn1003-6059.201802007

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Abstract The existing deep learning method is insufficient to extract features in the text sentiment classification task. To solve the drawback, a text sentiment classification algorithm based on the double channel convolutional neural network with extended features and a dynamic pooling is presented. Firstly, various word features influencing the sentiment orientation of text, such as emotional word, part of speech, adverb of degree, negative word and punctuation, are combined to obtain an extended text feature. Then, the word vector feature and the extended text feature are used as two individual channels of the convolutional neural network, and a new dynamic k-max pooling strategy is adopted to improve the efficiency of feature extraction. The experimental results on standard English datasets demonstrate that the proposed algorithm achieves better classification efficiency than traditional convolutional neural network algorithm with single channel, and it is more advantageous compared with some elitist text sentiment classification algorithms.

Key words： Text Sentiment Classification Convolutional Neural Network Double Channel Extended Feature Dynamic k-max Pooling

Received: 29 August 2017

ZTFLH:

TP 18

Fund:Supported by National Natural Science Foundation of China(No.61672065,61375059)

About author:: SHEN Chang, master student. His research interests include text mining and machine learning.JI Junzhong(Corresponding author), Ph.D., professor. His research interests include data mining, machine learning, swarm intelligence and bioinformatics.

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SHEN Chang,JI Junzhong. Text Sentiment Classification Algorithm Based on Double Channel Convolutional Neural Network[J]. , 2018, 31(2): 158-166.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/158

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