Suicide is among the leading causes of death in China, however, technical approaches toward preventing suicide are challenging and remain under development. Recently, several suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. Up to now, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. We explore linguistic features using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an Fmeasure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.