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Depressive Emotion Recognition Based on Behavioral Data

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Human Centered Computing (HCC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11354))

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Abstract

With the increase of pressure in people’s lives, depression has become one of the most common mental illness worldwide. The wide use of social media provides a new platform for depression recognition based on people’s behavioral data. This study utilizes the linguistical psychological characteristics of Weibo users to predict users’ depression level. The model adopts the Gaussian process regression algorithm, sets the PUK kernel as the kernel function, applies the forward-backward search method to select feature, and uses five-fold cross-validation to evaluate performance of the model. This study finally established a prediction model with a correlation coefficient of 0.5189, which achieved a medium correlation in the psychological definition, and provided a more accurate method for the auxiliary diagnosis of depression.

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Acknowledgement

The authors gratefully acknowledge the generous support from National Basic Research Program of China (2014CB744600).

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Authors and Affiliations

  1. Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Yue Su, Huijia Zheng, Xiaoqian Liu & Tingshao Zhu

  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Yue Su

  3. St. Mark’s School, Southborough, MA, 01772, USA

    Huijia Zheng

Authors
  1. Yue Su
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  2. Huijia Zheng
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  3. Xiaoqian Liu
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  4. Tingshao Zhu
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Corresponding author

Correspondence to Tingshao Zhu .

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Editors and Affiliations

  1. South China Normal University, Guangzhou, China

    Yong Tang

  2. Wuhan University of Technology, Wuhan City, China

    Qiaohong Zu

  3. CINVESTAV, Mexico City, Mexico

    José G. Rodríguez García

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Su, Y., Zheng, H., Liu, X., Zhu, T. (2019). Depressive Emotion Recognition Based on Behavioral Data. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-15127-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15126-3

  • Online ISBN: 978-3-030-15127-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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