Text Sentiment Classification Algorithm Based on Double Channel Convolutional Neural Network
SHEN Chang1, JI Junzhong1
1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124
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.
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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2018, Vol. 31 
