Authors:
Mai Mostafa
;
Alia El Bolock
and
Slim Abdennadher
Affiliation:
German University in Cairo, Egypt
Keyword(s):
Cognitive Distortions, Cognitive Behavioral Therapy, Mental Health, Machine Learning, Deep Learning, Natural Language Processing.
Abstract:
Cognitive distortions are negative thinking patterns that people adopt. Left undetected, it could lead to developing mental health problems. The goal of cognitive behavioral therapy is to correct and change cognitive distortions that in turn help with the recovery from mental illnesses such as depression and anxiety, overcoming addictions, and facing common life challenges. The aim of this study is to provide a machine learning solution for the automatic detection and classification of common cognitive distortions from journaling texts. Relatively few works have focused on exploring machine learning solutions and tools in the context of cognitive-behavioral therapy. And, given the rising popularity of online therapy programs, this tool could be used for instant feedback, and would also be a helpful service for therapists and psychiatrists to initiate and ease the detection of cognitive distortions. In this study, we provide a novel dataset that we used to train machine learning and d
eep learning algorithms. We then employed the best- performing model in an easy-to-use user interface.
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