Identifying cognitive distortion by convolutional neural network based text classification

Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identi...

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Main Authors: Xing, Zhenchang, Zhao, Xuejiao, Miao, Chunyan
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/89482
http://hdl.handle.net/10220/47264
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-894822019-12-10T14:32:14Z Identifying cognitive distortion by convolutional neural network based text classification Xing, Zhenchang Zhao, Xuejiao Miao, Chunyan School of Computer Science and Engineering NTU-UBC Research Centre of Excellence in Active Living for the Elderly Cognitive Distortion Word Embedding DRNTU::Engineering::Computer science and engineering Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identify the cognitive distortion. But these natural language materials are always diverse and desultory which affect the efficiency and accuracy of identification. In this research, we propose a model called ICODLE (Identifying Cognitive Distortion by Deep Learning) which utilizes the daily narration or diaries of the patients to identify the forms of the cognitive distortion. ICODLE collect the daily narration and diaries from the authoritative books and webpages in CBT (Cognitive-Behavioral Therapy) domain. Then ICODLE creates the database of the 10 forms of cognitive distortion which were defined by David D. Burns. By utilizing the advanced deep learning techniques (e.g., Word Embedding, CNN (Convolutional Neural Network), etc.), ICODLE can identify the forms of the patients' cognitive distortions without the features extraction. ICODLE can effectively assist the patients and the cognitive-behavioral therapists to diagnose the cognitive distortions. ICODLE also benefit to build up the online persuasion system. NRF (Natl Research Foundation, S’pore) Accepted version 2018-12-28T02:03:16Z 2019-12-06T17:26:41Z 2018-12-28T02:03:16Z 2019-12-06T17:26:41Z 2017 2017 Journal Article Zhao, X., Miao, C., & Xing, Z. (2017). Identifying cognitive distortion by convolutional neural network based text classification. International Journal of Information Technology, 23(1), 1-12. https://hdl.handle.net/10356/89482 http://hdl.handle.net/10220/47264 208285 en International Journal of Information Technology © 2017 Singapore Computer Society. This is the author created version of a work that has been peer reviewed and accepted for publication by International Journal of Information Technology, Singapore Computer Society. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. 12 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Cognitive Distortion
Word Embedding
DRNTU::Engineering::Computer science and engineering
spellingShingle Cognitive Distortion
Word Embedding
DRNTU::Engineering::Computer science and engineering
Xing, Zhenchang
Zhao, Xuejiao
Miao, Chunyan
Identifying cognitive distortion by convolutional neural network based text classification
description Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identify the cognitive distortion. But these natural language materials are always diverse and desultory which affect the efficiency and accuracy of identification. In this research, we propose a model called ICODLE (Identifying Cognitive Distortion by Deep Learning) which utilizes the daily narration or diaries of the patients to identify the forms of the cognitive distortion. ICODLE collect the daily narration and diaries from the authoritative books and webpages in CBT (Cognitive-Behavioral Therapy) domain. Then ICODLE creates the database of the 10 forms of cognitive distortion which were defined by David D. Burns. By utilizing the advanced deep learning techniques (e.g., Word Embedding, CNN (Convolutional Neural Network), etc.), ICODLE can identify the forms of the patients' cognitive distortions without the features extraction. ICODLE can effectively assist the patients and the cognitive-behavioral therapists to diagnose the cognitive distortions. ICODLE also benefit to build up the online persuasion system.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Xing, Zhenchang
Zhao, Xuejiao
Miao, Chunyan
format Article
author Xing, Zhenchang
Zhao, Xuejiao
Miao, Chunyan
author_sort Xing, Zhenchang
title Identifying cognitive distortion by convolutional neural network based text classification
title_short Identifying cognitive distortion by convolutional neural network based text classification
title_full Identifying cognitive distortion by convolutional neural network based text classification
title_fullStr Identifying cognitive distortion by convolutional neural network based text classification
title_full_unstemmed Identifying cognitive distortion by convolutional neural network based text classification
title_sort identifying cognitive distortion by convolutional neural network based text classification
publishDate 2018
url https://hdl.handle.net/10356/89482
http://hdl.handle.net/10220/47264
_version_ 1681039003576434688