@inproceedings{lichouri-etal-2021-preprocessing,
title = "Preprocessing Solutions for Detection of Sarcasm and Sentiment for {A}rabic",
author = "Lichouri, Mohamed and
Abbas, Mourad and
Benaziz, Besma and
Zitouni, Aicha and
Lounnas, Khaled",
editor = "Habash, Nizar and
Bouamor, Houda and
Hajj, Hazem and
Magdy, Walid and
Zaghouani, Wajdi and
Bougares, Fethi and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Touileb, Samia",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wanlp-1.49",
pages = "376--380",
abstract = "This paper describes our approach to detecting Sentiment and Sarcasm for Arabic in the ArSarcasm 2021 shared task. Data preprocessing is a crucial task for a successful learning, that is why we applied a set of preprocessing steps to the dataset before training two classifiers, namely Linear Support Vector Classifier (LSVC) and Bidirectional Long Short Term Memory (BiLSTM). The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33.71{\%} and 57.80{\%} for sarcasm and sentiment detection, respectively.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lichouri-etal-2021-preprocessing">
<titleInfo>
<title>Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mohamed</namePart>
<namePart type="family">Lichouri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mourad</namePart>
<namePart type="family">Abbas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Besma</namePart>
<namePart type="family">Benaziz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aicha</namePart>
<namePart type="family">Zitouni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khaled</namePart>
<namePart type="family">Lounnas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Arabic Natural Language Processing Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nizar</namePart>
<namePart type="family">Habash</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hazem</namePart>
<namePart type="family">Hajj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Walid</namePart>
<namePart type="family">Magdy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wajdi</namePart>
<namePart type="family">Zaghouani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fethi</namePart>
<namePart type="family">Bougares</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nadi</namePart>
<namePart type="family">Tomeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ibrahim</namePart>
<namePart type="family">Abu Farha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samia</namePart>
<namePart type="family">Touileb</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Kyiv, Ukraine (Virtual)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes our approach to detecting Sentiment and Sarcasm for Arabic in the ArSarcasm 2021 shared task. Data preprocessing is a crucial task for a successful learning, that is why we applied a set of preprocessing steps to the dataset before training two classifiers, namely Linear Support Vector Classifier (LSVC) and Bidirectional Long Short Term Memory (BiLSTM). The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33.71% and 57.80% for sarcasm and sentiment detection, respectively.</abstract>
<identifier type="citekey">lichouri-etal-2021-preprocessing</identifier>
<location>
<url>https://aclanthology.org/2021.wanlp-1.49</url>
</location>
<part>
<date>2021-04</date>
<extent unit="page">
<start>376</start>
<end>380</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic
%A Lichouri, Mohamed
%A Abbas, Mourad
%A Benaziz, Besma
%A Zitouni, Aicha
%A Lounnas, Khaled
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F lichouri-etal-2021-preprocessing
%X This paper describes our approach to detecting Sentiment and Sarcasm for Arabic in the ArSarcasm 2021 shared task. Data preprocessing is a crucial task for a successful learning, that is why we applied a set of preprocessing steps to the dataset before training two classifiers, namely Linear Support Vector Classifier (LSVC) and Bidirectional Long Short Term Memory (BiLSTM). The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33.71% and 57.80% for sarcasm and sentiment detection, respectively.
%U https://aclanthology.org/2021.wanlp-1.49
%P 376-380
Markdown (Informal)
[Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic](https://aclanthology.org/2021.wanlp-1.49) (Lichouri et al., WANLP 2021)
ACL