@inproceedings{malko-etal-2021-demonstrating,
title = "Demonstrating the Reliability of Self-Annotated Emotion Data",
author = "Malko, Anton and
Paris, Cecile and
Duenser, Andreas and
Kangas, Maria and
Molla, Diego and
Sparks, Ross and
Wan, Stephen",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.clpsych-1.5",
doi = "10.18653/v1/2021.clpsych-1.5",
pages = "45--54",
abstract = "Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="malko-etal-2021-demonstrating">
<titleInfo>
<title>Demonstrating the Reliability of Self-Annotated Emotion Data</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anton</namePart>
<namePart type="family">Malko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cecile</namePart>
<namePart type="family">Paris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">Duenser</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Kangas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diego</namePart>
<namePart type="family">Molla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ross</namePart>
<namePart type="family">Sparks</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stephen</namePart>
<namePart type="family">Wan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nazli</namePart>
<namePart type="family">Goharian</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">Andrew</namePart>
<namePart type="family">Yates</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Molly</namePart>
<namePart type="family">Ireland</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<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">Rebecca</namePart>
<namePart type="family">Resnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.</abstract>
<identifier type="citekey">malko-etal-2021-demonstrating</identifier>
<identifier type="doi">10.18653/v1/2021.clpsych-1.5</identifier>
<location>
<url>https://aclanthology.org/2021.clpsych-1.5</url>
</location>
<part>
<date>2021-06</date>
<extent unit="page">
<start>45</start>
<end>54</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Demonstrating the Reliability of Self-Annotated Emotion Data
%A Malko, Anton
%A Paris, Cecile
%A Duenser, Andreas
%A Kangas, Maria
%A Molla, Diego
%A Sparks, Ross
%A Wan, Stephen
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F malko-etal-2021-demonstrating
%X Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.
%R 10.18653/v1/2021.clpsych-1.5
%U https://aclanthology.org/2021.clpsych-1.5
%U https://doi.org/10.18653/v1/2021.clpsych-1.5
%P 45-54
Markdown (Informal)
[Demonstrating the Reliability of Self-Annotated Emotion Data](https://aclanthology.org/2021.clpsych-1.5) (Malko et al., CLPsych 2021)
ACL
- Anton Malko, Cecile Paris, Andreas Duenser, Maria Kangas, Diego Molla, Ross Sparks, and Stephen Wan. 2021. Demonstrating the Reliability of Self-Annotated Emotion Data. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 45–54, Online. Association for Computational Linguistics.