# Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT
## 1 Introduction  
Although people enjoy a higher standard of living today, new challenges such as rapid changes in living and working environments and human relationships can contribute to mental fatigue, leading to depression [1] and negative impacts on physical health. To address this issue, we aim to develop a method for early detection of depressive symptoms based on people's conversations. Many studies have been conducted on detecting depressive symptoms from users using user interviews and social media. [PERSON] et al. [2] used Twitter to build depressed and non-depressed datasets, and [PERSON] et al. [3] constructed a dataset with nine categories related to the DSM-5 from Reddit. Other researchers focus on developing new models for detecting depression based on text, such as [PERSON] et al. [4], who analyzed data from the subreddits 'r/SuicideWatch' and 'r/depression' using machine learning techniques, and [PERSON] et al. [5], who developed a deep-learning-based prediction model for early detection of depression using social media data. Some studies explore multi-modal datasets, such as [PERSON] et al. [6], who proposed an automated depression detection method that uses voice signals and language content from patient interviews. However, previous studies detecting depressive emotions focus on binary classification problems, i.e., whether users are depressed. Consequently, the datasets are primarily structured in this way, and there are relatively few studies on predicting the intensity of depression or classifying complex depressive emotions. To address this gap, in this paper, we introduce two new datasets: the Best-Worst Scaling [7] (BWS) dataset and the DSM-5 dataset, which are designed for detecting the intensity of depressive emotions and complex depressive emotions, respectively, labeled according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [8] (DSM-5) criteria. We also develop a Best-Worst Scaling annotation tool [9] using Flask and MySQL to assist in annotation. We employ the DistilBERT [10] language model to train and infer these datasets andcompare their performance with other machine learning and deep learning algorithms. Our proposed model architecture, AuD, is designed to quickly deduce depression intensity, complex depressive emotions, and high attention score tokens. We show the superiority of our model through performance comparisons with other algorithms, such as BERT [11] and ELECTRA [12]. Given that fast inference speed and superior performance are critical in real-time service environments such as chatbots, we suggest that the AuD model is well-suited for depression-related detection tasks. Table 1 shows a sample of the Best-Worst Scaling (BWS) dataset. In this dataset, each user utterance consists of a single sentence and is assigned a score between 0 and 1. These scores are then converted to a scale of 0 to 16 to prepare the dataset for training the model. Our classification system for depression is based on the DSM-5 guidelines published by the American Psychiatric Association (APA) in 2013. Specifically, we categorize depression into nine distinct types corresponding to the symptoms listed in the DSM-5. To be diagnosed with major depressive disorder [13] (MDD), a person must exhibit at least five symptoms for two weeks or longer. Table 2 represents the nine symptoms that form the basis of our complex depressive emotion categories. In addition, we add a daily label to classify user utterances that are unrelated to depression. Table 3 shows a sample of the DSM-5 dataset. Each user utterance in this dataset consists of a single sentence and is assigned a label based on the DSM-5 classification system presented in Table 2.  
This paper is organized into six sections. Section 1 serves as the introduction, providing an overview of the research problem and objectives. Section 2 presents a comprehensive