Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

Abstract

Background As a common mental disease, depression seriously affects people’s physical and mental health. According to the statistics of the World Health Organization, depression is one of the main reasons for suicide and self-harm events in the world. Therefore, strengthening depression detection can effectively reduce the occurrence of suicide or self-harm events so as to save more people and families. With the development of computer technology, some researchers are trying to apply natural language processing techniques to detect people who are depressed automatically. Many existing feature engineering methods for depression detection are based on emotional characteristics, but these methods do not consider high-level emotional semantic information. The current deep learning methods for depression detection cannot accurately extract effective emotional semantic information. Objective In this paper, we propose an emotion-based attention network, including a semantic understanding network and an emotion understanding network, which can capture the high-level emotional semantic information effectively to improve the depression detection task. Methods The semantic understanding network module is used to capture the contextual semantic information. The emotion understanding network module is used to capture the emotional semantic information. There are two units in the emotion understanding network module, including a positive emotion understanding unit and a negative emotion understanding unit, which are used to capture the positive emotional information and the negative emotional information, respectively. We further proposed a dynamic fusion strategy in the emotion understanding network module to fuse the positive emotional information and the negative emotional information. Results We evaluated our method on the Reddit data set. The experimental results showed that the proposed emotion-based attention network model achieved an accuracy, precision, recall, and F-measure of 91.30%, 91.91%, 96.15%, and 93.98%, respectively, which are comparable results compared with state-of-the-art methods. Conclusions The experimental results showed that our model is competitive with the state-of-the-art models. The semantic understanding network module, the emotion understanding network module, and the dynamic fusion strategy are effective modules for depression detection. In addition, the experimental results verified that the emotional semantic information was effective in depression detection.

Depression Speech Recognition With a Three-Dimensional Convolutional Network

“…In the depression detection scenario, the attention mechanism also played an excellent role. Lu et al ( 2021 ) proposed an emotion-based attention network that can capture high-level emotional semantic information and effectively improve depression detection tasks. A dynamic fusion strategy is proposed to integrate positive and negative emotional information.…”

MHA: a multimodal hierarchical attention model for depression detection in social media

“…Song et al [13] constructed four FAN models with good performance and high interpretability based on psychological research, and used the data on social media for depression detection. Ren et al [14] proposed an attention network that can discover the high-level semantic information and emotional information hidden in the text, and verified the effectiveness of the model by using the data in the social network. Mallol-Ragolta et al [27] and Xezonaki et al [28] used a hierarchical attentionbased model to extract language features from clinical visit records to detect whether individuals have depression.…”

“…If all data of users are considered to be of equal importance, it is possible to ignore important information that affects the classification of the model, resulting in the wrong prediction results given by the model. Therefore, it is necessary to introduce attention mechanism into depression detection, which can enable the model to automatically screen out the information that plays an important role in the prediction results [ 12 – 14 ]. However, most of the current depression detection studies based on multimodality only use the attention mechanism within a certain type of data.…”

Influence of preoperative depression on clinical outcomes after cervical laminoplasty: A retrospective study

“…Depression is a highly prevalent mental disorder. There are about 322 million patients with depression worldwide, and the prevalence rate is about 4.4% ( 16 ). As many as 40% of people have depression symptoms at a young age, and the incidence rate of depression reaches a peak between 50 and 60 years old ( 17 ).…”