Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89482
Title: Identifying cognitive distortion by convolutional neural network based text classification
Authors: Xing, Zhenchang
Zhao, Xuejiao
Miao, Chunyan
Keywords: Cognitive Distortion
Word Embedding
DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Source: 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.
Series/Report no.: International Journal of Information Technology
Abstract: 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.
URI: https://hdl.handle.net/10356/89482
http://hdl.handle.net/10220/47264
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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