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Trends and Applications in Knowledge Discovery and Data Mining

Volume 7867 of the series Lecture Notes in Computer Science pp 201-213

A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network

  • Xinyu WangAffiliated withBeijing University of Posts and Telecommunications (BUPT)
  • , Chunhong ZhangAffiliated withBeijing University of Posts and Telecommunications (BUPT)
  • , Yang JiAffiliated withBeijing University of Posts and Telecommunications (BUPT)
  • , Li SunAffiliated withBeijing University of Posts and Telecommunications (BUPT)
  • , Leijia WuAffiliated withUniversity of Technology
  • , Zhana BaoAffiliated withGraduate School of Global Information and Telecommunication Studies, Waseda University

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Abstract

Datasets originating from social networks are valuable to many fields such as sociology and psychology. But the supports from technical perspective are far from enough, and specific approaches are urgently in need. This paper applies data mining to psychology area for detecting depressed users in social network services. Firstly, a sentiment analysis method is proposed utilizing vocabulary and man-made rules to calculate the depression inclination of each micro-blog. Secondly, a depression detection model is constructed based on the proposed method and 10 features of depressed users derived from psychological research. Then 180 users and 3 kinds of classifiers are used to verify the model, whose precisions are all around 80%. Also, the significance of each feature is analyzed. Lastly, an application is developed within the proposed model for mental health monitoring online. This study is supported by some psychologists, and facilitates them in data-centric aspect in turn.

Keywords

data mining Chinese sentiment analysis depression