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AuthorAl-Sallab, Ahmad
AuthorBaly, Ramy
AuthorHajj, Hazem
AuthorShaban, Khaled Bashir
AuthorEl-Hajj, Wassim
AuthorBadaro, Gilbert
Available date2021-01-27T11:06:56Z
Publication Date2017
Publication NameACM Transactions on Asian and Low-Resource Language Information Processing
ResourceScopus
ISSN23754699
URIhttp://dx.doi.org/10.1145/3086575
URIhttp://hdl.handle.net/10576/17527
AbstractWhile research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A Recursive Deep Learning Model for Opinion Mining in Arabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature. ACM.
Languageen
PublisherAssociation for Computing Machinery
SubjectDeep Learning
Opinion mining in Arabic
Recursive Auto Encoder
Recursive Neural Networks
TitleAROMA: A recursive deep learning model for opinion mining in Arabic as a low resource language
TypeArticle Review
Issue Number4
Volume Number16


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