Early risk detection of depression from social media posts using Hierarchical Attention Networks

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Tamilarasan Ramasamy, Dr. J. Jayanthi


Efficient mental health diagnosis is improving continuously, yet many cases go undetected. Early detection of depression can potentially prevent people from mental illness and live a better life. There are many ways to monitor depression in people; the most obvious one is to monitor the messages posted by people on social media platforms. In recent years, detecting early depression from social media posts has been a focused research area. In this paper, we use Hierarchical Attention Networks, a Deep Learning-based method to classify whether the users are depressed or not using their historical, social media posts.

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