A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

Ramy Baly, Gilbert Badaro, Georges El-Khoury, Rawan Moukalled, Rita Aoun, Hazem Hajj, Wassim El-Hajj, Nizar Habash, Khaled Shaban


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.
Anthology ID:
W17-1314
Volume:
Proceedings of the Third Arabic Natural Language Processing Workshop
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
WANLP
SIG:
SEMITIC
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–118
Language:
URL:
https://aclanthology.org/W17-1314
DOI:
10.18653/v1/W17-1314
Bibkey:
Cite (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.
Cite (Informal):
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models (Baly et al., WANLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W17-1314.pdf
Data
ASTD