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Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotionrelated analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotionsas features and examined five popular machine learning classifiers in the task of identifying users with selfreportedmental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based featuresand combined features showed promising improvements to the performance on this task.

proceedings of the ... annual hawaii international conference on system sciences/proceedings of the annual hawaii international conference on system sciences

Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions