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        <title>Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects</title>
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            <title>Proceedings of the Fourth Arabic Natural Language Processing Workshop</title>
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            <namePart type="given">Lamia</namePart>
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            <namePart type="given">Walid</namePart>
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            <namePart type="given">Mahmoud</namePart>
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    <abstract>Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.</abstract>
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    <identifier type="doi">10.18653/v1/W19-4604</identifier>
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        <date>2019-08</date>
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            <start>30</start>
            <end>39</end>
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%0 Conference Proceedings
%T Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects
%A Mulki, Hala
%A Haddad, Hatem
%A Gridach, Mourad
%A Babaoğlu, Ismail
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F mulki-etal-2019-syntax
%X Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.
%R 10.18653/v1/W19-4604
%U https://aclanthology.org/W19-4604
%U https://doi.org/10.18653/v1/W19-4604
%P 30-39

Markdown (Informal)

[Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects](https://aclanthology.org/W19-4604) (Mulki et al., WANLP 2019)

• Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects (Mulki et al., WANLP 2019)

ACL

• Hala Mulki, Hatem Haddad, Mourad Gridach, and Ismail Babaoğlu. 2019. Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 30–39, Florence, Italy. Association for Computational Linguistics.