@inproceedings{burkhardt-etal-2022-comparing,
title = "Comparing emotion feature extraction approaches for predicting depression and anxiety",
author = "Burkhardt, Hannah and
Pullmann, Michael and
Hull, Thomas and
Are{\'a}n, Patricia and
Cohen, Trevor",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.9/",
doi = "10.18653/v1/2022.clpsych-1.9",
pages = "105--115",
abstract = "The increasing adoption of message-based behavioral therapy enables new approaches to assessing mental health using linguistic analysis of patient-generated text. Word counting approaches have demonstrated utility for linguistic feature extraction, but deep learning methods hold additional promise given recent advances in this area. We evaluated the utility of emotion features extracted using a BERT-based model in comparison to emotions extracted using word counts as predictors of symptom severity in a large set of messages from text-based therapy sessions involving over 6,500 unique patients, accompanied by data from repeatedly administered symptom scale measurements. BERT-based emotion features explained more variance in regression models of symptom severity, and improved predictive modeling of scale-derived diagnostic categories. However, LIWC categories that are not directly related to emotions provided valuable and complementary information for modeling of symptom severity, indicating a role for both approaches in inferring the mental states underlying patient-generated language."
}
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%0 Conference Proceedings
%T Comparing emotion feature extraction approaches for predicting depression and anxiety
%A Burkhardt, Hannah
%A Pullmann, Michael
%A Hull, Thomas
%A Areán, Patricia
%A Cohen, Trevor
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F burkhardt-etal-2022-comparing
%X The increasing adoption of message-based behavioral therapy enables new approaches to assessing mental health using linguistic analysis of patient-generated text. Word counting approaches have demonstrated utility for linguistic feature extraction, but deep learning methods hold additional promise given recent advances in this area. We evaluated the utility of emotion features extracted using a BERT-based model in comparison to emotions extracted using word counts as predictors of symptom severity in a large set of messages from text-based therapy sessions involving over 6,500 unique patients, accompanied by data from repeatedly administered symptom scale measurements. BERT-based emotion features explained more variance in regression models of symptom severity, and improved predictive modeling of scale-derived diagnostic categories. However, LIWC categories that are not directly related to emotions provided valuable and complementary information for modeling of symptom severity, indicating a role for both approaches in inferring the mental states underlying patient-generated language.
%R 10.18653/v1/2022.clpsych-1.9
%U https://aclanthology.org/2022.clpsych-1.9/
%U https://doi.org/10.18653/v1/2022.clpsych-1.9
%P 105-115
Markdown (Informal)
[Comparing emotion feature extraction approaches for predicting depression and anxiety](https://aclanthology.org/2022.clpsych-1.9/) (Burkhardt et al., CLPsych 2022)
ACL