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AuthorBaly, Ramy
AuthorBadaro, Gilbert
AuthorEl-Khoury, Georges
AuthorMoukalled, Rawan
AuthorAoun, Rita
AuthorHajj, Hazem
AuthorEl-Hajj, Wassim
AuthorHabash, Nizar
AuthorShaban, Khaled
Available date2022-12-21T10:01:45Z
Publication Date2017
Publication NameWANLP 2017, co-located with EACL 2017 - 3rd Arabic Natural Language Processing Workshop, Proceedings of the Workshop
ResourceScopus
URIhttp://dx.doi.org/10.18653/v1/W17-1314
URIhttp://hdl.handle.net/10576/37482
AbstractOpinion 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 non-conformation 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. 2017 Association for Computational Linguistics
SponsorThis work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherAssociation for Computational Linguistics (ACL)
SubjectData mining
Deep learning
Linguistics
Morphology
Sentiment analysis
Advanced researches
Analytical studies
Characterization studies
Code-switching
Dialectal variation
Linguistic phenomena
Opinion mining
Performance
Source of noise
State of the art
Social networking (online)
TitleA Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
TypeConference Paper
Pagination110-118
dc.accessType Abstract Only


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