@inproceedings{ive-etal-2018-hierarchical,
title = "Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health",
author = "Ive, Julia and
Gkotsis, George and
Dutta, Rina and
Stewart, Robert and
Velupillai, Sumithra",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud{'}hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0607",
doi = "10.18653/v1/W18-0607",
pages = "69--77",
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.",
}
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%0 Conference Proceedings
%T Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
%A Ive, Julia
%A Gkotsis, George
%A Dutta, Rina
%A Stewart, Robert
%A Velupillai, Sumithra
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F ive-etal-2018-hierarchical
%X 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.
%R 10.18653/v1/W18-0607
%U https://aclanthology.org/W18-0607
%U https://doi.org/10.18653/v1/W18-0607
%P 69-77
Markdown (Informal)
[Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health](https://aclanthology.org/W18-0607) (Ive et al., CLPsych 2018)
ACL