A Deep Learning Based Chatbot for Campus Psychological Therapy
This addresses mental health issues for adolescents on campus, but it is incremental as it applies existing deep learning methods to a specific domain.
The authors tackled the problem of detecting negative emotions and preventing depression among adolescents by developing Evebot, a Seq2seq-based chatbot for campus psychological therapy, which showed better results in increasing positivity compared to other chatbots in a one-month user study.
In this paper, we propose Evebot, an innovative, sequence to sequence (Seq2seq) based, fully generative conversational system for the diagnosis of negative emotions and prevention of depression through positively suggestive responses. The system consists of an assembly of deep-learning based models, including Bi-LSTM based model for detecting negative emotions of users and obtaining psychological counselling related corpus for training the chatbot, anti-language sequence to sequence neural network, and maximum mutual information (MMI) model. As adolescents are reluctant to show their negative emotions in physical interaction, traditional methods of emotion analysis and comforting methods may not work. Therefore, this system puts emphasis on using virtual platform to detect signs of depression or anxiety, channel adolescents' stress and mood, and thus prevent the emergence of mental illness. We launched the integrated chatbot system onto an online platform for real-world campus applications. Through a one-month user study, we observe better results in the increase in positivity than other public chatbots in the control group.