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Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and

Bin Cui¹, Jian Wang¹, Hongfei Lin¹

¹College of Computer Science and Technology, Dalian University of Technology, Dalian, China.

JMIR Medical Informatics

|August 9, 2022

Summary

This summary is machine-generated.

This study introduces an emotion-based network for detecting depression on social media. The model effectively extracts emotional features and identifies key posts, improving depression detection accuracy.

Keywords:

depression detection emotion-based reinforcement emotional semantic features sentence-level attention social media

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Area of Science:

Natural Language Processing
Computational Linguistics
Affective Computing

Background:

Depression detection using social media data is a growing area in NLP.
Existing methods often use all historical posts and struggle with deep emotional feature extraction.
A novel approach is needed to effectively identify depression indicators based on emotional states.

Purpose of the Study:

To develop an emotion-based reinforcement attention network for social media depression detection.
To enhance the extraction of deep emotional semantic features.
To improve the selection of depression indicator posts using emotional states.

Main Methods:

A two-component model: an emotion extraction network and a reinforcement learning (RL) attention network.
The emotion extraction network captures deep emotional semantic information.
The RL attention network selects depression indicator posts based on emotional states, followed by classification.

Main Results:

The proposed model achieved superior performance on a multimodal depression dataset.
Key performance metrics included accuracy (90.6%), precision (91.2%), recall (89.7%), and F1-score (90.4%).
Outperformed state-of-the-art baseline methods in depression detection.

Conclusions:

The developed model effectively identifies depression tendencies from user historical posts.
The emotion extraction and RL selection layers significantly improve detection accuracy.
A sentence-level attention layer successfully captures core posts indicative of depression.