Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1011
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dc.contributor.authorMulki, Hala-
dc.contributor.authorHaddad, Hatem-
dc.contributor.authorGridach, Mourad-
dc.contributor.authorBabaoglu, İsmail-
dc.date.accessioned2021-12-13T10:32:20Z-
dc.date.available2021-12-13T10:32:20Z-
dc.date.issued2021-
dc.identifier.issn1351-3249-
dc.identifier.issn1469-8110-
dc.identifier.urihttps://doi.org/10.1017/S135132492000008X-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1011-
dc.description.abstractArabic sentiment analysis models have recently employed compositional paragraph or sentence embedding features to represent the informal Arabic dialectal content. These embeddings are mostly composed via ordered, syntax-aware composition functions and learned within deep neural network architectures. With the differences in the syntactic structure and words' order among the Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant, sentiment-specific n-gram embeddings for sentiment analysis of several Arabic dialects. The novelty of the proposed model is illustrated through its features and architecture. In the proposed model, the sentiment is expressed by embeddings, composed via the unordered additive composition function and learned within a shallow neural architecture. To evaluate the generated embeddings, they were compared with the state-of-the art word/paragraph embeddings. This involved investigating their efficiency, as expressive sentiment features, based on the visualisation maps constructed for our n-gram embeddings and word2vec/doc2vec. In addition, using several Eastern/Western Arabic datasets of single-dialect and multi-dialectal contents, the ability of our embeddings to recognise the sentiment was investigated against word/paragraph embeddings-based models. This comparison was performed within both shallow and deep neural network architectures and with two unordered composition functions employed. The results revealed that the introduced syntax-ignorant embeddings could represent single and combinations of different dialects efficiently, as our shallow sentiment analysis model, trained with the proposed n-gram embeddings, could outperform the word2vec/doc2vec models and rival deep neural architectures consuming, remarkably, less training time.en_US
dc.language.isoenen_US
dc.publisherCAMBRIDGE UNIV PRESSen_US
dc.relation.ispartofNATURAL LANGUAGE ENGINEERINGen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectn-gram embeddingsen_US
dc.subjectUnordered compositionalityen_US
dc.subjectArabic dialectsen_US
dc.subjectSentiment analysisen_US
dc.titleSyntax-ignorant N-gram embeddings for dialectal Arabic sentiment analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1017/S135132492000008X-
dc.identifier.scopus2-s2.0-85082685504en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authoridhaddad, hatem/0000-0003-3599-7229-
dc.authorwosidhaddad, hatem/ABD-1530-2021-
dc.identifier.volume27en_US
dc.identifier.issue3en_US
dc.identifier.startpage315en_US
dc.identifier.endpage338en_US
dc.identifier.wosWOS:000656232400003en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57200388232-
dc.authorscopusid22734490100-
dc.authorscopusid50161532700-
dc.authorscopusid23097339300-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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