Abstract
As a response to the urgent demand of methods that help detect depression at early stage, the work presented in this paper has adopted sentiment analysis techniques to analyse users’ contributions of social network to detect potential depression. A prototype has been developed, aiming at demonstrating the mechanism of the approach and potential social effect that may be delivered. The contributions include a depressive sentiment knowledge base and an algorithm to analyse textual data for depression detection.
The original version of this chapter was revised: An acknowledgement has been added. The erratum to this chapter is available at DOI: 10.1007/978-3-319-49586-6_61
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-49586-6_61
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Acknowledgement
Special thanks go to the “Helping Minds” team from University of Southern Queensland, Australia, specifically, Heather Wallace, Declan Keyes-Bevan, Jason Alexander, and Jodie Coles, for implementation of the demo system.
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Tao, X., Zhou, X., Zhang, J., Yong, J. (2016). Sentiment Analysis for Depression Detection on Social Networks. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_59
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DOI: https://doi.org/10.1007/978-3-319-49586-6_59
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