Abstract
Depression has become a serious global disease which can harm to mental health. Diagnosis of depression is a complicated process, and needs a great deal of specialist knowledge. A person’s social network behavior can reflect his/her real psychological. The traditional process of diagnosing depression requires not only close coordination of depressed people but also too much professional knowledge. And depressed people maybe have fewer and fewer social activities. In this paper, machine learning is used to diagnosis whether a person has depression. We use a SVM algorithm to create the depression diagnosis model based on short text. And we validate this model using a data set of Sina Micro-blog users’ Micro-blog content and depression label data gotten by certificate of diagnosis. Our work makes three important contributions. Firstly, we show how to diagnosis depression based on short text published on the social network. Secondly, we build diagnosis model of depression based on SVM. And last and thirdly, we give the experimental results that validate our method, the accuracy of whether or not depression can reach 93.47%.
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Acknowledgement
The author would like to thank the anonymous reviewers for their valuable comments. This work is supported by the National Natural Science Foundation of China (Grant Number: 61602491).
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Zheng, J., Bian, J., Jia, J. (2019). Diagnosis of Depression Based on Short Text. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_65
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DOI: https://doi.org/10.1007/978-3-030-37429-7_65
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