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.
N1 - Publisher Copyright:
© 2023 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85187555441&partnerID=8YFLogxK
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 -