@inproceedings{allen-etal-2019-convsent,
title = "{C}onv{S}ent at {CLP}sych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on {R}eddit",
author = "Allen, Kristen and
Bagroy, Shrey and
Davis, Alex and
Krishnamurti, Tamar",
editor = "Niederhoffer, Kate and
Hollingshead, Kristy and
Resnik, Philip and
Resnik, Rebecca and
Loveys, Kate",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3024",
doi = "10.18653/v1/W19-3024",
pages = "182--187",
abstract = "This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users{'} suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system{'}s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="allen-etal-2019-convsent">
<titleInfo>
<title>ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kristen</namePart>
<namePart type="family">Allen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shrey</namePart>
<namePart type="family">Bagroy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alex</namePart>
<namePart type="family">Davis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tamar</namePart>
<namePart type="family">Krishnamurti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kate</namePart>
<namePart type="family">Niederhoffer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kristy</namePart>
<namePart type="family">Hollingshead</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philip</namePart>
<namePart type="family">Resnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rebecca</namePart>
<namePart type="family">Resnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kate</namePart>
<namePart type="family">Loveys</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Minneapolis, Minnesota</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.</abstract>
<identifier type="citekey">allen-etal-2019-convsent</identifier>
<identifier type="doi">10.18653/v1/W19-3024</identifier>
<location>
<url>https://aclanthology.org/W19-3024</url>
</location>
<part>
<date>2019-06</date>
<extent unit="page">
<start>182</start>
<end>187</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit
%A Allen, Kristen
%A Bagroy, Shrey
%A Davis, Alex
%A Krishnamurti, Tamar
%Y Niederhoffer, Kate
%Y Hollingshead, Kristy
%Y Resnik, Philip
%Y Resnik, Rebecca
%Y Loveys, Kate
%S Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F allen-etal-2019-convsent
%X This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.
%R 10.18653/v1/W19-3024
%U https://aclanthology.org/W19-3024
%U https://doi.org/10.18653/v1/W19-3024
%P 182-187
Markdown (Informal)
[ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit](https://aclanthology.org/W19-3024) (Allen et al., CLPsych 2019)
ACL