[논문]딥러닝 기반 소셜미디어 한글 텍스트 우울 경향 분석

박서정; 이수빈; 김우정; 송민

doi:10.3743/kosim.2022.39.1.091

[국내논문] 딥러닝 기반 소셜미디어 한글 텍스트 우울 경향 분석
A Deep Learning-based Depression Trend Analysis of Korean on Social Media 원문보기

정보관리학회지 = Journal of the Korean society for information management, v.39 no.1, 2022년, pp.91 - 117

박서정 (Department of Library and Information Science, Yonsei University) , 이수빈 (Department of Library and Information Science, Yonsei University) , 김우정 (Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine) , 송민 (Department of Library and Information Science, Yonsei University)

초록
AI-Helper

국내를 비롯하여 전 세계적으로 우울증 환자 수가 매년 증가하는 추세이다. 그러나 대다수의 정신질환 환자들은 자신이 질병을 앓고 있다는 사실을 인식하지 못해서 적절한 치료가 이루어지지 않고 있다. 우울 증상이 방치되면 자살과 불안, 기타 심리적인 문제로 발전될 수 있기에 우울증의 조기 발견과 치료는 정신건강 증진에 있어 매우 중요하다. 이러한 문제점을 개선하기 위해 본 연구에서는 한국어 소셜 미디어 텍스트를 활용한 딥러닝 기반의 우울 경향 모델을 제시하였다. 네이버 지식인, 네이버 블로그, 하이닥, 트위터에서 데이터수집을 한 뒤 DSM-5 주요 우울 장애 진단 기준을 활용하여 우울 증상 개수에 따라 클래스를 구분하여 주석을 달았다. 이후 구축한 말뭉치의 클래스 별 특성을 살펴보고자 TF-IDF 분석과 동시 출현 단어 분석을 실시하였다. 또한, 다양한 텍스트 특징을 활용하여 우울 경향 분류 모델을 생성하기 위해 단어 임베딩과 사전 기반 감성 분석, LDA 토픽 모델링을 수행하였다. 이를 통해 문헌 별로 임베딩된 텍스트와 감성 점수, 토픽 번호를 산출하여 텍스트 특징으로 사용하였다. 그 결과 임베딩된 텍스트에 문서의 감성 점수와 토픽을 모두 결합하여 KorBERT 알고리즘을 기반으로 우울 경향을 분류하였을 때 가장 높은 정확률인 83.28%를 달성하는 것을 확인하였다. 본 연구는 다양한 텍스트 특징을 활용하여 보다 성능이 개선된 한국어 우울 경향 분류 모델을 구축함에 따라, 한국 온라인 커뮤니티 이용자 중 잠재적인 우울증 환자를 조기에 발견해 빠른 치료 및 예방이 가능하도록 하여 한국 사회의 정신건강 증진에 도움을 줄 수 있는 기반을 마련했다는 점에서 의의를 지닌다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (16)

표 Number of data collected from 4 data sources
표 Class separation criteria
표 Data annotation result
표 Data annotation example
그림 Research flow
그림 The variation of perplexity by number of topics
표 Top 30 words of TF-IDF value in Non-depressed literature
표 Top 30 words of TF-IDF value in Latent depressed literature
표 Top 30 words of TF-IDF value in Depressed tendency literature
그림 Co-word network in Non depression related texts
표 Community detection results of non-depression related texts
그림 Co-word network in latent depression related texts
그림 Co-word network in high depression related texts
표 Community detection results of latent depression related texts
표 Community detection results of high depression related texts
표 Results of a depression tendency classification model in accuracy rates

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

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