Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

Julia Ive, George Gkotsis, Rina Dutta, Robert Stewart, Sumithra Velupillai

Abstract
Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.
Anthology ID:
W18-0607
Volume:
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
Month:
June
Year:
2018
Address:
New Orleans, LA
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–77
Language:
URL:
https://aclanthology.org/W18-0607
DOI:
10.18653/v1/W18-0607
Bibkey:
Cite (ACL):
Julia Ive, George Gkotsis, Rina Dutta, Robert Stewart, and Sumithra Velupillai. 2018. Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 69–77, New Orleans, LA. Association for Computational Linguistics.
Cite (Informal):
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health (Ive et al., CLPsych 2018)
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https://preview.aclanthology.org/ingestion-script-update/W18-0607.pdf
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