@inproceedings{guellil-etal-2021-one,
title = "{ONE}: Toward {ONE} model, {ONE} algorithm, {ONE} corpus dedicated to sentiment analysis of {A}rabic/{A}rabizi and its dialects",
author = "Guellil, Imane and
Azouaou, Faical and
Benali, Fodil and
Ala-Eddine, Hachani",
editor = "De Clercq, Orphee and
Balahur, Alexandra and
Sedoc, Joao and
Barriere, Valentin and
Tafreshi, Shabnam and
Buechel, Sven and
Hoste, Veronique",
booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wassa-1.25/",
pages = "236--249",
abstract = "Arabic is the official language of 22 countries, spoken by more than 400 million speakers. Each one of this country use at least on dialect for daily life conversation. Then, Arabic has at least 22 dialects. Each dialect can be written in Arabic or Arabizi Scripts. The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script. Following this technique means constructing 46 different resources (by including the Modern Standard Arabic, MSA) for handling only one language. In this paper, we extract ONE corpus, and we propose ONE algorithm to automatically construct ONE training corpus using ONE classification model architecture for sentiment analysis MSA and different dialects. After manually reviewing the training corpus, the obtained results outperform all the research literature results for the targeted test corpora."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="guellil-etal-2021-one">
<titleInfo>
<title>ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects</title>
</titleInfo>
<name type="personal">
<namePart type="given">Imane</namePart>
<namePart type="family">Guellil</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Faical</namePart>
<namePart type="family">Azouaou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fodil</namePart>
<namePart type="family">Benali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hachani</namePart>
<namePart type="family">Ala-Eddine</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 Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Orphee</namePart>
<namePart type="family">De Clercq</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexandra</namePart>
<namePart type="family">Balahur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joao</namePart>
<namePart type="family">Sedoc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Valentin</namePart>
<namePart type="family">Barriere</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shabnam</namePart>
<namePart type="family">Tafreshi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sven</namePart>
<namePart type="family">Buechel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</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>Arabic is the official language of 22 countries, spoken by more than 400 million speakers. Each one of this country use at least on dialect for daily life conversation. Then, Arabic has at least 22 dialects. Each dialect can be written in Arabic or Arabizi Scripts. The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script. Following this technique means constructing 46 different resources (by including the Modern Standard Arabic, MSA) for handling only one language. In this paper, we extract ONE corpus, and we propose ONE algorithm to automatically construct ONE training corpus using ONE classification model architecture for sentiment analysis MSA and different dialects. After manually reviewing the training corpus, the obtained results outperform all the research literature results for the targeted test corpora.</abstract>
<identifier type="citekey">guellil-etal-2021-one</identifier>
<location>
<url>https://aclanthology.org/2021.wassa-1.25/</url>
</location>
<part>
<date>2021-04</date>
<extent unit="page">
<start>236</start>
<end>249</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects
%A Guellil, Imane
%A Azouaou, Faical
%A Benali, Fodil
%A Ala-Eddine, Hachani
%Y De Clercq, Orphee
%Y Balahur, Alexandra
%Y Sedoc, Joao
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Buechel, Sven
%Y Hoste, Veronique
%S Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F guellil-etal-2021-one
%X Arabic is the official language of 22 countries, spoken by more than 400 million speakers. Each one of this country use at least on dialect for daily life conversation. Then, Arabic has at least 22 dialects. Each dialect can be written in Arabic or Arabizi Scripts. The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script. Following this technique means constructing 46 different resources (by including the Modern Standard Arabic, MSA) for handling only one language. In this paper, we extract ONE corpus, and we propose ONE algorithm to automatically construct ONE training corpus using ONE classification model architecture for sentiment analysis MSA and different dialects. After manually reviewing the training corpus, the obtained results outperform all the research literature results for the targeted test corpora.
%U https://aclanthology.org/2021.wassa-1.25/
%P 236-249
Markdown (Informal)
[ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects](https://aclanthology.org/2021.wassa-1.25/) (Guellil et al., WASSA 2021)
ACL