Date of Publication

7-3-2022

Document Type

Dissertation/Thesis

Degree Name

Master of Science in Computer Science

College

College of Computer Studies

Department/Unit

Software Technology

Thesis Advisor

Ethel Chua Joy Ong

Defense Panel Chair

Charibeth K. Cheng

Defense Panel Member

Judith J. Azcarraga

Abstract/Summary

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.

Abstract Format

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Language

English

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Embargo Period

7-7-2022

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