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

BACKGROUND: Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select...

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Autores principales: Cui, Bin, Wang, Jian, Lin, Hongfei, Zhang, Yijia, Yang, Liang, Xu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399877/
https://www.ncbi.nlm.nih.gov/pubmed/35943770
http://dx.doi.org/10.2196/37818
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author Cui, Bin
Wang, Jian
Lin, Hongfei
Zhang, Yijia
Yang, Liang
Xu, Bo
author_facet Cui, Bin
Wang, Jian
Lin, Hongfei
Zhang, Yijia
Yang, Liang
Xu, Bo
author_sort Cui, Bin
collection PubMed
description BACKGROUND: Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states. OBJECTIVE: This study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media. METHODS: The proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection. RESULTS: Experimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6%, 91.2%, 89.7%, and 90.4%, respectively. CONCLUSIONS: The proposed model utilizes historical posts of users to effectively identify users’ depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts.
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spelling pubmed-93998772022-08-25 Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation Cui, Bin Wang, Jian Lin, Hongfei Zhang, Yijia Yang, Liang Xu, Bo JMIR Med Inform Original Paper BACKGROUND: Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states. OBJECTIVE: This study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media. METHODS: The proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection. RESULTS: Experimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6%, 91.2%, 89.7%, and 90.4%, respectively. CONCLUSIONS: The proposed model utilizes historical posts of users to effectively identify users’ depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts. JMIR Publications 2022-08-09 /pmc/articles/PMC9399877/ /pubmed/35943770 http://dx.doi.org/10.2196/37818 Text en ©Bin Cui, Jian Wang, Hongfei Lin, Yijia Zhang, Liang Yang, Bo Xu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 09.08.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Cui, Bin
Wang, Jian
Lin, Hongfei
Zhang, Yijia
Yang, Liang
Xu, Bo
Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title_full Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title_fullStr Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title_full_unstemmed Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title_short Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation
title_sort emotion-based reinforcement attention network for depression detection on social media: algorithm development and validation
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399877/
https://www.ncbi.nlm.nih.gov/pubmed/35943770
http://dx.doi.org/10.2196/37818
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