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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, C.C.: Social Network Data Analytics. Springer, New York (2011)
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)
World Health Organization, http://www.who.int/en/
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)
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)
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)
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Now Publisher Inc. (2008)
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)
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)
Go, A., Huang, L., Bhayani, R.: Twitter Sentiment Classification using Distant Supervision. Project Report, CS224N (2009)
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)
Parikh, R., Movassate, M.: Sentiment Analysis of User-Generated Twitter Updates using Various Classification Techniques. Final Report, CS224N (2009)
Tan, S.B., Zhang, J.: An Empirical Study of Sentiment Analysis for Chinese Documents. Expert Systems with Applications 34, 2262–2269 (2008)
NII Test Collection for IR Systems, http://research.nii.ac.jp/ntcir/
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)
Institute of Computing Technology, Chinese Lexical Analysis System, http://ictclas.org/
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)
Sina Micro-blog Open Platform, http://open.weibo.com
Han, J.W., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kauf-mann, San Francisco (2006)
Witten, I.H., Frank, E.: Data Mining: Pratical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., Bao, Z. (2013). A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-40319-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40318-7
Online ISBN: 978-3-642-40319-4
eBook Packages: Computer ScienceComputer Science (R0)