Automatic Psychological Text Analysis using Recurrent Neural Networks
S.X. Zhang (TU Delft - Electrical Engineering, Mathematics and Computer Science)
W.P. Brinkman – Mentor (TU Delft - Interactive Intelligence)
Merijn Bruijnes – Graduation committee member (TU Delft - Interactive Intelligence)
Hayley Hung – Coach (TU Delft - Pattern Recognition and Bioinformatics)
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GitHub containing code
https://github.com/Mirijam1/Automatic_Psychological_Text_Analysis_RNNOther than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.