An Improved Convolutional Neural Network for Text Classification

This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN,...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-11, Vol.2066 (1), p.12091
Main Authors: Fan, Xiaojing, Runa, A, Pei, Zhili, Jiang, Mingyang
Format: Article
Language:English
Subjects:
Classification
CNN
Dropout
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Summary:This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2066/1/012091