@inproceedings{baly-etal-2017-characterization,
title = "A Characterization Study of {A}rabic {T}witter Data with a Benchmarking for State-of-the-Art Opinion Mining Models",
author = "Baly, Ramy and
Badaro, Gilbert and
El-Khoury, Georges and
Moukalled, Rawan and
Aoun, Rita and
Hajj, Hazem and
El-Hajj, Wassim and
Habash, Nizar and
Shaban, Khaled",
editor = "Habash, Nizar and
Diab, Mona and
Darwish, Kareem and
El-Hajj, Wassim and
Al-Khalifa, Hend and
Bouamor, Houda and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1314",
doi = "10.18653/v1/W17-1314",
pages = "110--118",
abstract = "Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic{'}s complex morphology.",
}
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<abstract>Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic’s complex morphology.</abstract>
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%0 Conference Proceedings
%T A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
%A Baly, Ramy
%A Badaro, Gilbert
%A El-Khoury, Georges
%A Moukalled, Rawan
%A Aoun, Rita
%A Hajj, Hazem
%A El-Hajj, Wassim
%A Habash, Nizar
%A Shaban, Khaled
%Y Habash, Nizar
%Y Diab, Mona
%Y Darwish, Kareem
%Y El-Hajj, Wassim
%Y Al-Khalifa, Hend
%Y Bouamor, Houda
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Third Arabic Natural Language Processing Workshop
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F baly-etal-2017-characterization
%X Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic’s complex morphology.
%R 10.18653/v1/W17-1314
%U https://aclanthology.org/W17-1314
%U https://doi.org/10.18653/v1/W17-1314
%P 110-118
Markdown (Informal)
[A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models](https://aclanthology.org/W17-1314) (Baly et al., WANLP 2017)
ACL
- Ramy Baly, Gilbert Badaro, Georges El-Khoury, Rawan Moukalled, Rita Aoun, Hazem Hajj, Wassim El-Hajj, Nizar Habash, and Khaled Shaban. 2017. A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models. In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 110–118, Valencia, Spain. Association for Computational Linguistics.