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Citation - WoS: 7
Citation - Scopus: 10
Syntax-Ignorant N-Gram Embeddings for Sentiment Analysis of Arabic Dialects
(ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2019) Mulki, Hala; Haddad, Hatem; Gridach, Mourad; Babaoglu, İsmail
Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.