Recognition of college students from Weibo with deep neural networks

Recognition of college students from Weibo with deep neural networks Classification of college students is a key to conduct further research on students. In this paper, we collect a set of samples and build deep neural network classifiers to recognize them. We also analyze the experiences and behaviors of the college students on Weibo. Firstly, we manually label 1502 student users and 1498 non-college students. Then, the data about their posts are crawled from Weibo to be transformed into input vectors by feature engineering techniques. Finally, classifiers are built based on two deep learning algorithms, including stacked autoencoders and deep belief network. Experimental results show that deep neural networks performs better than other machine learning algorithms and the classification of the college students can achieve a very high accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

Recognition of college students from Weibo with deep neural networks

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0515-1
Publisher site
See Article on Publisher Site

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