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Detecting Depression from Human Conversations

Depression is the silent killer of the new age. Although depression is considered a mental illness it can affect physical health. Most often people ignore it until physical or acute mental symptoms start showing up. One way to early detect symptoms of depression is to analyze what people are talking about - their conversations. This is an attractive solution as it is much less noninvasive than conventional medical tests and has the potential to be quite accurate. In this paper, we explored this idea. We used transcripts of conversation data and analyzed the text using ten different machine learning algorithms including LSTM, SVM, Ensemble methods, Random forest, and Decision tree. As our goal is to find any patterns in spoken language people used to express depression - screams, cheers, mumbles, whines, stutters, murmurs, etc., we applied the learning algorithms on original transcripts of the conversations. We found that LSTM performed best (about 94% accuracy) in finding depressed dialogues followed by naïve Bayes (about 69.05% accuracy).