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Deep Learning Based Topic Identification and Categorization: Mining Diabetes-Related Topics on Chinese Health Websites

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9642))

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Abstract

As millions of people are diagnosed with diabetes every year, the demand for information about diabetes continues to increase. China is one of the countries with a large population of diabetes patients. Many Chinese health websites provide diabetes related news and articles. However, because most of the online articles are uncategorized or lack a clear topic and theme, users often cannot find their topics of interest effectively and efficiently. The problem of health topic identification and categorization on Chinese websites cannot be easily addressed by applying existing approaches and methods, which have been used for English documents, in a straightforward manner. To address this problem and meet users’ demand for diabetes related information needs, we propose a deep learning based framework to identify and categorize topics related to diabetes in online Chinese articles. Our experiments using datasets with over 19,000 online articles showed that the framework achieved a higher effectiveness and accuracy in categorizing diabetes related topics than most of the state-of-the-art benchmark approaches.

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Notes

  1. 1.

    We consider only F-measure here because it is a comprehensive metric incorporating both Precision and Recall.

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Acknowledgements

The research presented in this paper has been funded by grants from the National High-tech R&D Program of China (Grant No. SS2015AA020102), the US NSF grant (IIP-1417181), the 1000-Talent program, and Tsinghua University Initiative Scientific Research Program.

Author information

Authors and Affiliations

  1. Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xinhuan Chen, Yong Zhang, Chunxiao Xing & Hsinchun Chen

  2. Department of Computer Information Systems, Bentley University, Waltham, USA

    Jennifer Xu

  3. MIS Department, University of Arizona, Tucson, USA

    Hsinchun Chen

Authors
  1. Xinhuan Chen
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  2. Yong Zhang
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  3. Jennifer Xu
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  4. Chunxiao Xing
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  5. Hsinchun Chen
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Corresponding author

Correspondence to Xinhuan Chen .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas, Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota, Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University, Beijing, China

    Xiaoyong Du

  5. Fudan University, Shanghai, China

    X. Sean Wang

  6. Rutgers, The State University of New Jer, New Brunswick, New Jersey, USA

    Hui Xiong

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Chen, X., Zhang, Y., Xu, J., Xing, C., Chen, H. (2016). Deep Learning Based Topic Identification and Categorization: Mining Diabetes-Related Topics on Chinese Health Websites. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-32025-0_30

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