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Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation
- Source :
- JMIR Medical Informatics
- Publication Year :
- 2021
- Publisher :
- JMIR Publications Inc., 2021.
-
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.
- Subjects :
- Feature engineering
Computer science
social media
emotion
Health Informatics
02 engineering and technology
Task (project management)
emotional semantic information
Health Information Management
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Social media
natural language processing
Depression (differential diagnoses)
Original Paper
algorithm
Recall
business.industry
Deep learning
deep learning
Mental health
dynamic fusion strategy
attention network
depression detection
020201 artificial intelligence & image processing
Artificial intelligence
business
mental health
Cognitive psychology
Computer technology
Subjects
Details
- ISSN :
- 22919694
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- JMIR Medical Informatics
- Accession number :
- edsair.doi.dedup.....aa2b92750272633a00c27265210e4347