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  • P-ISSN1013-0799
  • E-ISSN2586-2073

Building and Analyzing Panic Disorder Social Media Corpus for Automatic Deep Learning Classification Model

Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2021, v.38 no.2, pp.153-172
https://doi.org/10.3743/KOSIM.2021.38.2.153






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Abstract

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

Submission Date
2021-05-17
Revised Date
2021-06-03
Accepted Date
2021-06-15

Journal of the Korean Society for Information Management