Automatic Psychological Text Analysis using Recurrent Neural Networks

Bachelor thesis (2021)

Authors

S.X. Zhang Electrical Engineering, Mathematics and Computer Science

Contributors

W.P. Brinkman Interactive Intelligence - EEMCS (supervisor 1)
M. Bruijnes Interactive Intelligence - EEMCS (supervisor 2)
H.S. Hung Pattern Recognition and Bioinformatics - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Mirijam Zhang
Recurrent Neural Network Psychological text analysis Cognitive behaviour therapy Therapy chatbot
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Schema therapy is a type of psychological treatment for people suffering from personality disorders. A schema is a core psychological state of mind that influences external behaviour through the development of coping styles. Current schema therapy is time inefficient and human-processed. Enabling automatic schema classification helps the overall goal of creating a chatbot that can classify schema modes from conversations. The goal of this research was to optimize a Recurrent Neural Network (RNN) model to classify patient’s schema modes from a dataset containing a recent emotional story from participants and are labeled with SMI questionnaire answers. Three RNN models were created: a binary classification Multilabel RNN, a binary classification Per-Schema RNN and a ordinal classification Per-Schema RNN. The results have shown that the binary classification Multilabel model scores an average F1-score of 0.48. The binary classification Per-Schema model scores an average F1-score of 0.49. While ordinal classification Per-Schema model performs with an average Spearman Coefficient of 0.15.