A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

• Main Authors: Wulamu Aziguli, Yuanyu Zhang, Yonghong Xie, Dezheng Zhang, Xiong Luo, Chunmiao Li, Yao Zhang
• Format: Article
• Language: English
• Published: Hindawi Limited 2017-01-01
• Series: Scientific Programming
• Online Access: http://dx.doi.org/10.1155/2017/3610378
• ISSN: 1058-9244; 1875-919X

Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.