Proceedings of the Fifth Workshop on Computational Linguistics and
          Clinical Psychology: From Keyboard to Clinic

Loveys, Kate; Niederhoffer, Kate; Prud’hommeaux, Emily; Resnik, Rebecca; Resnik, Philip

doi:10.18653/v1/w18-06

2018

DOI: 10.18653/v1/w18-06

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Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Kate Loveys¹,

Kate Niederhoffer²,

Emily Prud’hommeaux³

et al.

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2022

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Cited by 1 publication

References 83 publications

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

Cui¹,

Wang²,

Lin³

et al. 2022

JMIR Med Inform

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

Cui¹,

Wang²,

Lin³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

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Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Cited by 1 publication

References 83 publications

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

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

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