Methodology of Labeling According to 9 Criteria of DSM-5

Abstract

Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used in Korea to identify depressive disorders, sets nine criteria for diagnosing depressive disorders. The lack of counseling personnel, including psychiatrists, and negative social perceptions of depressive disorders prevent counselors from being treated for depressive disorders. Natural language processing-based artificial intelligence (AI) services such as chatbots can help fill this need, but labeled datasets are needed to train AI services. In this study we collected data from AI Hub wellness consultations and crawls of the Reddit website to augment and build word dictionaries and analyze morphemes using the Kind Korean Morpheme Analyzer and Word2Vec. The collected datasets were labeled based on word dictionaries built according to nine DSM-5 depressive disorder diagnostic criteria.

Keywords

• DSM-5
• natural language processing
• morphological analysis
• multi labeling
• embedding