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Arabic tweeps dialect prediction based on machine learning approach

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 2 (2021)

DOI
10.11591/ijece.v11i2.pp1627-1633
ISSN
2722-2578, 2088-8708
Published
2021-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.

Collaboration network of the authors

4 authors · 5 co-authorship links · 3 across different organisations

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  • Higher Institute for Applied Sciences and Technology
  • Damascus University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Riad SonbolHigher Institute for Applied Sciences and Technology2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Arabic tweeps dialect prediction based on machine learning approach</dc:title><dc:creator>Alrifai, Khaled</dc:creator><dc:creator>Rebdawi, Ghaida</dc:creator><dc:creator>Ghneim, Nada</dc:creator><dc:description>&lt;jats:p&gt;In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i2.pp1627-1633</dc:identifier><dc:identifier>10.11591/ijece.v11i2.pp1627-1633</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 2 (2021); 1627</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21530/14687</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>