  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.
