Detecting suicide ideation from Sina microblog

Suicide is becoming a serious problem, and how to prevent suicide has become a very important research topic. The development of Social Network System (SNS) provides an ideal platform to monitor persons' suicidal ideation. Based on Sina microblog (Weibo), this paper proposes a real-time monitoring system detecting users' suicidal ideation. From 59046 posts collected with labels of either suicide or non-suicide, we extract new features based on content and emotion. Finally, four different classifiers including Support Vector Machine(SVM), Multinomial Naive Bayes(MultiNB), Logistic Regression(LR), Multi Layer Perception(MLP) are used to construct classified model respectively. Experimental results show that it is possible to detect suicidal ideation from microblogs, and the result of MLP is the best, whose F1 is up to 67.6%.