Kim Nam Hyeok, Kim Ji Min, Park Da Mi, Ji Su Ryeon, Kim Jong Woo
Department of Mathematics, Hanyang University, Seoul, Republic of Korea.
Business Administration, Hanyang University, Seoul, Republic of Korea.
Digit Health. 2022 Jul 17;8:20552076221114204. doi: 10.1177/20552076221114204. eCollection 2022 Jan-Dec.
Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users' depression based on their results.
First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user's sentence is related to depression, and the 0-9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9. Then the depression classifier, which is a logistic regression model, was generated to classify the sentence writer's depression. These trained sentence classifiers and the depression classifier were used to analyze the social media textual data of users and establish their depression.
Our experimental results showed that the proposed depression classifier showed 68.3% average accuracy, which was better than the baseline depression classifier that used only the Y/N sentence classifier and had 53.3% average accuracy.
This study is significant in that it demonstrates the possibility of determining depression from only social media users' textual data.
尽管现代人的抑郁症已成为一个主要的社会问题，但心理健康服务的使用率却很低。本研究的目的是根据患者健康问卷-9（Patient Health Questionnaire-9）中的九种抑郁症状对社交媒体用户撰写的句子进行分类，利用自然语言处理根据结果评估用户的自然抑郁情况。
首先，训练两个句子分类器：是/否句子分类器，用于对用户的句子是否与抑郁相关进行分类；0-9句子分类器，用于根据患者健康问卷-9的抑郁症状学对用户句子进行进一步分类。然后生成作为逻辑回归模型的抑郁分类器，对句子作者的抑郁情况进行分类。这些经过训练的句子分类器和抑郁分类器用于分析用户的社交媒体文本数据并确定他们的抑郁情况。
我们的实验结果表明，所提出的抑郁分类器平均准确率为68.3%，优于仅使用是/否句子分类器且平均准确率为53.3%的基线抑郁分类器。
本研究的意义在于，它证明了仅从社交媒体用户的文本数据中确定抑郁情况的可能性。
