Advertisement

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

  • Xinyu Wang
  • Chunhong Zhang
  • Yang Ji
  • Li Sun
  • Leijia Wu
  • Zhana Bao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7867)

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, C.C.: Social Network Data Analytics. Springer, New York (2011)Google Scholar
  2. 2.
    Hsieh, Y., Bolan, J.E.: Predicting Processing Difficulty in Chineses Syntactic Ambiguity Resolution: A Parallel Approach. Poster, The 84th Annual Meeting of the Linguistic Society of America, Baltimore, MD (2010)Google Scholar
  3. 3.
    World Health Organization, http://www.who.int/en/
  4. 4.
    Ramirez-Esparza, N., Chung, C.K., Kacewicz, E., Pennebaker, J.W.: The Psychology of Word Use in Depression Forums in English and in Spanish: Testing Two Text Analytic Approaches. In: Proceedings of the International Conference on Weblogs and Social Media, pp. 102–108. AAAI Press, Menlo Park (2008)Google Scholar
  5. 5.
    Moreno, M., Jelenchick, L., Egan, K., Cox, E., Young, H., Gannon, K., et al.: Feeling Bad on Facebook: Depression Disclosures by College Students on Social Networking Site. Depression and Anxiety 28, 447–455 (2011)CrossRefGoogle Scholar
  6. 6.
    Ji, Y.: Social Displacement, Homophily and Depression Levels: The Case of Zoufan on a Chinese Social Network Site. Cyberpsychology, Behavior, and Social Networking. For Peer Review (2012)Google Scholar
  7. 7.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Now Publisher Inc. (2008)Google Scholar
  8. 8.
    Davidiv, D., Tsur, O., Rappoport, A.: Enhanced Sentiment Learning Using Twitter Hash-tags and Smileys. In: Proceedings of the 23rd International Conference on Computational in Linguistics, pp. 241–249. Coling 2010 Organizing Committee, Beijing (2010)Google Scholar
  9. 9.
    Barbosa, L., Feng, J.L.: Robust Sentiment Detection on Twitter from Biased and Noisy Data. In: Proceedings of the 23rd International Conference on Computational in Linguistics, pp. 36–44. Coling 2010 Organizing Committee, Beijing (2010)Google Scholar
  10. 10.
    Go, A., Huang, L., Bhayani, R.: Twitter Sentiment Classification using Distant Supervision. Project Report, CS224N (2009)Google Scholar
  11. 11.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent Twitter Sentiment Classification. In: Proceeding of 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160 (2011)Google Scholar
  12. 12.
    Parikh, R., Movassate, M.: Sentiment Analysis of User-Generated Twitter Updates using Various Classification Techniques. Final Report, CS224N (2009)Google Scholar
  13. 13.
    Tan, S.B., Zhang, J.: An Empirical Study of Sentiment Analysis for Chinese Documents. Expert Systems with Applications 34, 2262–2269 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    NII Test Collection for IR Systems, http://research.nii.ac.jp/ntcir/
  15. 15.
    Dong, Z., Dong, Q.: HowNet—A Hybrid Language and Knowledge Resource. In: Proceedings of International Conference on Natural Language Processing and Knowledge Engineering, pp. 820–824. IEEE Press, Los Alamitos (2003)Google Scholar
  16. 16.
    Institute of Computing Technology, Chinese Lexical Analysis System, http://ictclas.org/
  17. 17.
    Zhang, C.L., Zeng, D., Li, J.X., Wang, F.Y., Zuo, W.L.: Sentiment Analysis of Chinese Documents: From Sentence to Document Level. Journal of the American Society for Information Science and Technology 60, 2474–2487 (2009)CrossRefGoogle Scholar
  18. 18.
    Sina Micro-blog Open Platform, http://open.weibo.com
  19. 19.
    Han, J.W., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kauf-mann, San Francisco (2006)Google Scholar
  20. 20.
    Witten, I.H., Frank, E.: Data Mining: Pratical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xinyu Wang
    • 1
  • Chunhong Zhang
    • 1
  • Yang Ji
    • 1
  • Li Sun
    • 1
  • Leijia Wu
    • 2
  • Zhana Bao
    • 3
  1. 1.Beijing University of Posts and Telecommunications (BUPT)BeijingChina
  2. 2.University of TechnologySydneyAustralia
  3. 3.Graduate School of Global Information and Telecommunication StudiesWaseda UniversityJapan

Personalised recommendations