Detection and Classification of Cognitive Distortions: A Literature Review

I. Putu Gede Hendra Suputra; Linawati Linawati; Nyoman Putra Sastra; Gede Sukadarmika; Ngurah Agus Sanjaya Er; Diana Purwitasari; I. Made Agus Setiawan

doi:10.1109/ICSGTEIS60500.2023.10424225

Detection and Classification of Cognitive Distortions: A Literature Review

I. Putu Gede Hendra Suputra^*
, Linawati Linawati
, Nyoman Putra Sastra
, Gede Sukadarmika
, Ngurah Agus Sanjaya Er
, Diana Purwitasari
, I. Made Agus Setiawan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Various machine learning and deep learning approaches have recently been applied to detecting and classifying cognitive distortions (CD). However, there are several challenges in detecting and classifying cognitive distortions. This paper outlines current research in CD detection and classification, challenges and problems. The first challenge lies in the limited availability and accessibility of public datasets. Another issue relates to CD domains, which involve short text data that may only partially represent a particular CD category. The performance of CD detection model has a relatively good value, while in general, the classification process has not shown promising results. One model that works well and consistently is BERT, both in terms of vector representation and as a classifier. In addition to the model, dataset availability and reliability remains an important issues to address.

Original language	English
Title of host publication	ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems
Subtitle of host publication	Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	166-171
Number of pages	6
ISBN (Electronic)	9798350382822
DOIs	https://doi.org/10.1109/ICSGTEIS60500.2023.10424225
Publication status	Published - 2023
Event	2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023 - Hybrid, Bali, Indonesia Duration: 2 Nov 2023 → 4 Nov 2023

Publication series

Name	Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS
ISSN (Print)	2831-3992
ISSN (Electronic)	2831-400X

Conference

Conference	2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023
Country/Territory	Indonesia
City	Hybrid, Bali
Period	2/11/23 → 4/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cognitive Distortion
Deep Learning
Machine Learning
Text Classification

Access to Document

10.1109/ICSGTEIS60500.2023.10424225

Cite this

Putu Gede Hendra Suputra, I., Linawati, L., Sastra, N. P., Sukadarmika, G., Er, N. A. S., Purwitasari, D., & Made Agus Setiawan, I. (2023). Detection and Classification of Cognitive Distortions: A Literature Review. In ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems: Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings (pp. 166-171). (Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSGTEIS60500.2023.10424225

Putu Gede Hendra Suputra, I. ; Linawati, Linawati ; Sastra, Nyoman Putra et al. / Detection and Classification of Cognitive Distortions : A Literature Review. ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems: Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 166-171 (Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS).

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title = "Detection and Classification of Cognitive Distortions: A Literature Review",

abstract = "Various machine learning and deep learning approaches have recently been applied to detecting and classifying cognitive distortions (CD). However, there are several challenges in detecting and classifying cognitive distortions. This paper outlines current research in CD detection and classification, challenges and problems. The first challenge lies in the limited availability and accessibility of public datasets. Another issue relates to CD domains, which involve short text data that may only partially represent a particular CD category. The performance of CD detection model has a relatively good value, while in general, the classification process has not shown promising results. One model that works well and consistently is BERT, both in terms of vector representation and as a classifier. In addition to the model, dataset availability and reliability remains an important issues to address.",

keywords = "Cognitive Distortion, Deep Learning, Machine Learning, Text Classification",

author = "\{Putu Gede Hendra Suputra\}, I. and Linawati Linawati and Sastra, \{Nyoman Putra\} and Gede Sukadarmika and Er, \{Ngurah Agus Sanjaya\} and Diana Purwitasari and \{Made Agus Setiawan\}, I.",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023 ; Conference date: 02-11-2023 Through 04-11-2023",

year = "2023",

doi = "10.1109/ICSGTEIS60500.2023.10424225",

language = "English",

series = "Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "166--171",

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}

Putu Gede Hendra Suputra, I, Linawati, L, Sastra, NP, Sukadarmika, G, Er, NAS, Purwitasari, D & Made Agus Setiawan, I 2023, Detection and Classification of Cognitive Distortions: A Literature Review. in ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems: Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings. Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS, Institute of Electrical and Electronics Engineers Inc., pp. 166-171, 2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023, Hybrid, Bali, Indonesia, 2/11/23. https://doi.org/10.1109/ICSGTEIS60500.2023.10424225

Detection and Classification of Cognitive Distortions: A Literature Review. / Putu Gede Hendra Suputra, I.; Linawati, Linawati; Sastra, Nyoman Putra et al.
ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems: Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 166-171 (Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection and Classification of Cognitive Distortions

T2 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

AU - Putu Gede Hendra Suputra, I.

AU - Linawati, Linawati

AU - Sastra, Nyoman Putra

AU - Sukadarmika, Gede

AU - Er, Ngurah Agus Sanjaya

AU - Purwitasari, Diana

AU - Made Agus Setiawan, I.

PY - 2023

Y1 - 2023

N2 - Various machine learning and deep learning approaches have recently been applied to detecting and classifying cognitive distortions (CD). However, there are several challenges in detecting and classifying cognitive distortions. This paper outlines current research in CD detection and classification, challenges and problems. The first challenge lies in the limited availability and accessibility of public datasets. Another issue relates to CD domains, which involve short text data that may only partially represent a particular CD category. The performance of CD detection model has a relatively good value, while in general, the classification process has not shown promising results. One model that works well and consistently is BERT, both in terms of vector representation and as a classifier. In addition to the model, dataset availability and reliability remains an important issues to address.

AB - Various machine learning and deep learning approaches have recently been applied to detecting and classifying cognitive distortions (CD). However, there are several challenges in detecting and classifying cognitive distortions. This paper outlines current research in CD detection and classification, challenges and problems. The first challenge lies in the limited availability and accessibility of public datasets. Another issue relates to CD domains, which involve short text data that may only partially represent a particular CD category. The performance of CD detection model has a relatively good value, while in general, the classification process has not shown promising results. One model that works well and consistently is BERT, both in terms of vector representation and as a classifier. In addition to the model, dataset availability and reliability remains an important issues to address.

KW - Cognitive Distortion

KW - Deep Learning

KW - Machine Learning

KW - Text Classification

UR - https://www.scopus.com/pages/publications/85187555441

U2 - 10.1109/ICSGTEIS60500.2023.10424225

DO - 10.1109/ICSGTEIS60500.2023.10424225

M3 - Conference contribution

AN - SCOPUS:85187555441

T3 - Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS

SP - 166

EP - 171

BT - ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 2 November 2023 through 4 November 2023

ER -

Putu Gede Hendra Suputra I, Linawati L, Sastra NP, Sukadarmika G, Er NAS, Purwitasari D et al. Detection and Classification of Cognitive Distortions: A Literature Review. In ICSGTEIS 2023 - 2023 International Conference on Smart-Green Technology in Electrical and Information Systems: Leveraging Sustainable Technologies Towards Energy Transition and Net Zero Emission, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 166-171. (Proceedings - International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS). doi: 10.1109/ICSGTEIS60500.2023.10424225

Detection and Classification of Cognitive Distortions: A Literature Review

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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