Abstract
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.
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This work was supported by the National Natural Science Foundation of China (Grant No. 71171068).
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Yu, X., Yu, H., Tian, XY. et al. Recognition of college students from Weibo with deep neural networks. Int. J. Mach. Learn. & Cyber. 8, 1447–1455 (2017). https://doi.org/10.1007/s13042-016-0515-1
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DOI: https://doi.org/10.1007/s13042-016-0515-1