VHope: An empathetic virtual hope chatbot using neural conversational model for students mental well-being

• Main Author: Beredo, Jackylyn
• Format: text
• Language: English
• Published: Animo Repository 2022
• Online Access: https://animorepository.dlsu.edu.ph/etdm_softtech/2; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1003&context=etdm_softtech
• Institution: De La Salle University

Mental health has always been a concern for everyone, globally and even in the Philippines. Natural conversational interfaces such as conversational agents or chatbots are proven to be effective in providing more accessible and non-stigmatizing mental health interventions but most of them focus on treating people and only a few helps in preventing mental illness by providing social support. More importantly, most of these chatbots are built with only rule-based or retrieval-based models which limits the chatbot’s input understanding, response generation, and the ability to freely adapt to the context of a conversation. This study resulted to the creation of VHope (Virtual Hope)’s hybrid design that combines a retrieval model with a natural conversation flow to help the agent to act as a therapist that can facilitate the conversation, and a generative model that can generate new and empathetic responses using neural networks to enrich the conversation. For the generative model, a neural conversational model DialoGPT was fine-tuned with EmpatheticDialogues dataset and a cleaned Well-being conversations. The fluency of the generative model was measured using the perplexity metric and the best performing model among the trained variations got a perplexity score of 9.977. In determining the user’s well-being state throughout the conversation, PERMA model was utilized. This model got an accuracy of 57% from comparing users’ manual questionnaire results over the automated detection and 59% accuracy based from the experts evaluation. Experts further commented that VHope was able to adapt and correct the well-being labels during the conversations. With this, 61% of the users maintained their well-being while 22% improved over the testing period. Overall results from experts validation of logs showed that the responses generated by VHope were 67% relevant, 78% human-like, and 79% empathic. Users also recognized Vhope as a non-judgemental human (15%) they can talk emotional stories with and a friend (5%) who can comfort him and listen to his stories.