@inproceedings{dragos-battistelli-2026-corpus,
title = "A Corpus-Based Comparison of two Approaches for Emotion Annotation in {F}rench Texts",
author = "Dragos, Valentina and
Battistelli, Delphine",
editor = "Bagdon, Christopher and
Vishnubhotla, Krishnapriya and
Lindquist, Kristen A. and
Ungar, Lyle and
Klinger, Roman and
Mohammad, Saif M.",
booktitle = "Proceedings of Computational Affective Science ({CAS}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.cas-1.4/",
doi = "10.63317/49jyy8yh5ht8",
pages = "38--46",
abstract = "Emotion annotation in texts remains a challenging task in the field of Natural Language Processing (NLP), as, unlike voice or images, texts might not only contain peculiar cues to express emotions. Methods for emotion annotation are based on lexicons or on machine learning techniques which are based on the use of manually annotated corpora. This paper aims to explore if and how the combination of these two types of methods might be useful for the annotation of emotions in texts. Four data sets are used for comparison of the two approaches, and then to investigate to what extent the results are distinct or complementary on three aspects: (i) identification of emotional sentences; (ii) identification of emotion categories; (iii) identification of one specific mode of expression of emotions called ``behavioral emotions'' (e.g. shout, cry). Findings show that not all emotions are equally easy to annotate, and, most specifically, the learning-based approach tends to over detect Admiration."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dragos-battistelli-2026-corpus">
<titleInfo>
<title>A Corpus-Based Comparison of two Approaches for Emotion Annotation in French Texts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Valentina</namePart>
<namePart type="family">Dragos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Delphine</namePart>
<namePart type="family">Battistelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of Computational Affective Science (CAS) @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christopher</namePart>
<namePart type="family">Bagdon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Krishnapriya</namePart>
<namePart type="family">Vishnubhotla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kristen</namePart>
<namePart type="given">A</namePart>
<namePart type="family">Lindquist</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lyle</namePart>
<namePart type="family">Ungar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roman</namePart>
<namePart type="family">Klinger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saif</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Mohammad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Emotion annotation in texts remains a challenging task in the field of Natural Language Processing (NLP), as, unlike voice or images, texts might not only contain peculiar cues to express emotions. Methods for emotion annotation are based on lexicons or on machine learning techniques which are based on the use of manually annotated corpora. This paper aims to explore if and how the combination of these two types of methods might be useful for the annotation of emotions in texts. Four data sets are used for comparison of the two approaches, and then to investigate to what extent the results are distinct or complementary on three aspects: (i) identification of emotional sentences; (ii) identification of emotion categories; (iii) identification of one specific mode of expression of emotions called “behavioral emotions” (e.g. shout, cry). Findings show that not all emotions are equally easy to annotate, and, most specifically, the learning-based approach tends to over detect Admiration.</abstract>
<identifier type="citekey">dragos-battistelli-2026-corpus</identifier>
<identifier type="doi">10.63317/49jyy8yh5ht8</identifier>
<location>
<url>https://aclanthology.org/2026.cas-1.4/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>38</start>
<end>46</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Corpus-Based Comparison of two Approaches for Emotion Annotation in French Texts
%A Dragos, Valentina
%A Battistelli, Delphine
%Y Bagdon, Christopher
%Y Vishnubhotla, Krishnapriya
%Y Lindquist, Kristen A.
%Y Ungar, Lyle
%Y Klinger, Roman
%Y Mohammad, Saif M.
%S Proceedings of Computational Affective Science (CAS) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F dragos-battistelli-2026-corpus
%X Emotion annotation in texts remains a challenging task in the field of Natural Language Processing (NLP), as, unlike voice or images, texts might not only contain peculiar cues to express emotions. Methods for emotion annotation are based on lexicons or on machine learning techniques which are based on the use of manually annotated corpora. This paper aims to explore if and how the combination of these two types of methods might be useful for the annotation of emotions in texts. Four data sets are used for comparison of the two approaches, and then to investigate to what extent the results are distinct or complementary on three aspects: (i) identification of emotional sentences; (ii) identification of emotion categories; (iii) identification of one specific mode of expression of emotions called “behavioral emotions” (e.g. shout, cry). Findings show that not all emotions are equally easy to annotate, and, most specifically, the learning-based approach tends to over detect Admiration.
%R 10.63317/49jyy8yh5ht8
%U https://aclanthology.org/2026.cas-1.4/
%U https://doi.org/10.63317/49jyy8yh5ht8
%P 38-46
Markdown (Informal)
[A Corpus-Based Comparison of two Approaches for Emotion Annotation in French Texts](https://aclanthology.org/2026.cas-1.4/) (Dragos & Battistelli, CAS 2026)
ACL