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Multi-kernel SVM based depression recognition using social media data

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Depression has become the world’s fourth major disease. Compared with the high incidence, however, the rate of depression medical treatment is very low because of the difficulty of diagnosis of mental problems. The social media opens one window to evaluate the users’ mental status. With the rapid development of Internet, people are accustomed to express their thoughts and feelings through social media. Thus social media provides a new way to find out the potential depressed people. In this paper, we propose a multi-kernel SVM based model to recognize the depressed people. Three categories of features, user microblog text, user profile and user behaviors, are extracted from their social media to describe users’ situations. According to the new characteristics of social media language, we build a special emotional dictionary consisted of text emotional dictionary and emoticon dictionary to extract microblog text features for word frequency statistics. Considering the heterogeneity between text feature and another two features, we employ multi-kernel SVM methods to adaptively select the optimal kernel for different features to find out users who may suffer from depression. Compared with Naive Bayes, Decision Trees, KNN, single-kernel SVM and ensemble method (libD3C), whose error reduction rates are 38, 43, 22, 21 and 11% respectively, the error rate of multi-kernel SVM method for identifying the depressed people is reduced to 16.54%. This indicates that the multi-kernel SVM method is the most appropriate way to find out depressed people based on social media data.

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This work is partly supported by National Program on Key Basic Research Project under Grant 2013CB329304, National Natural Science Foundation of China under Grant 61222210 and New Century Excellent Talents in University under Grant NCET-12-0399.

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Correspondence to Jianwu Dang.

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Peng, Z., Hu, Q. & Dang, J. Multi-kernel SVM based depression recognition using social media data. Int. J. Mach. Learn. & Cyber. 10, 43–57 (2019).

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