Abstract
An important part of Cognitive Behavioral Therapy (CBT) is to recognize and restructure certain negative thinking patterns that are also known as cognitive distortions. This project aims to detect these distortions using natural language processing. We compare and contrast different types of linguistic features as well as different classification algorithms and explore the limitations of applying these techniques on a small dataset. We find that pretrained Sentence-BERT embeddings to train an SVM classifier yields the best results with an F1-score of 0.79. Lastly, we discuss how this work provides insights into the types of linguistic features that are inherent in cognitive distortions.
Cite
CITATION STYLE
Shreevastava, S., & Foltz, P. W. (2021). Detecting Cognitive Distortions from Patient-Therapist Interactions. In Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021 (pp. 151–158). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.clpsych-1.17
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