AROMA

Autor: Ahmad Al-Sallab, Ramy Baly, Wassim El-Hajj, Hazem Hajj, Gilbert Badaro, Khaled Bashir Shaban
Rok vydání: 2017
Předmět:
Zdroj: ACM Transactions on Asian and Low-Resource Language Information Processing. 16:1-20
ISSN: 2375-4702
2375-4699
Popis: While 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 R ecursive Deep Learning Model for O pinion M ining in A rabic (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.
Databáze: OpenAIRE
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