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http://dx.doi.org/10.3743/KOSIM.2022.39.1.091

A Deep Learning-based Depression Trend Analysis of Korean on Social Media  

Park, Seojeong (Department of Library and Information Science, Yonsei University)
Lee, Soobin (Department of Library and Information Science, Yonsei University)
Kim, Woo Jung (Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine)
Song, Min (Department of Library and Information Science, Yonsei University)
Publication Information
Journal of the Korean Society for information Management / v.39, no.1, 2022 , pp. 91-117 More about this Journal
Abstract
The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.
Keywords
topic modeling; deep learning; sentiment analysis; social media;
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