The abundance and growing usage of social media has given an unprecedented access to users' social accounts for studying people's thoughts and sentiments. In this work, we are interested in tracking in-dividual's emotional states and more specifically suicidal ideation in mi-croblogging services. We propose a probabilistic framework that models user's online activities as a sequence of psychological states over time and predicts the emotional states by incorporating the context history. Based on Conditional Random Fields, our model is able to provide comprehensive interpretations of the relationship between the risk factors and psychological states. We evaluated our approach within real case studies of Twitter' users that have demonstrated a serious change in their emotional states and online behaviour. Our experiments show that the model is able to identify suicidal ideation with high precision and good recall with substantial improvements on state-of-the-art methods.