TY - CHAP
T1 - Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
AU - Ive, Julia
AU - Gkotsis, George
AU - Dutta, Rina
AU - Stewart, Robert
AU - Velupillai, Sumithra
N1 - Publisher Copyright:
© 2018 Association for Computational Linguistics
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061048924&partnerID=8YFLogxK
M3 - Conference paper
AN - SCOPUS:85061048924
T3 - Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018
SP - 69
EP - 77
BT - Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology
A2 - Loveys, Kate
A2 - Niederhoffer, Kate
A2 - Prud�hommeaux, Emily
A2 - Resnik, Rebecca
A2 - Resnik, Philip
PB - Association for Computational Linguistics (ACL)
T2 - 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018
Y2 - 5 June 2018
ER -
