Empirical Evaluation of Word Representations on Arabic Sentiment Analysis

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dc.contributor.authorGridach, Mourad
dc.contributor.authorHaddad, Hatem
dc.contributor.authorMulki, Hala
dc.date.accessioned2020-03-26T19:53:45Z
dc.date.available2020-03-26T19:53:45Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Arabic Language Processing (ICALP) -- OCT 11-12, 2017 -- Fez, MOROCCOen_US
dc.description.abstractSentiment analysis is the Natural Language Processing (NLP) task that aims to classify text to different classes such as positive, negative or neutral. In this paper, we focus on sentiment analysis for Arabic language. Most of the previous works use machine learning techniques combined with hand engineering features to do Arabic sentiment analysis (ASA). More recently, Deep Neural Networks (DNNs) were widely used for this task especially for English languages. In this work, we developed a system called CNN-ASAWR where we investigate the use of Convolutional Neural Networks (CNNs) for ASA on 2 datasets: ASTD and SemEval 2017 datasets. We explore the importance of various unsupervised word representations learned from unannotated corpora. Experimental results showed that we were able to outperform the previous state-of-the-art systems on the datasets without using any kind of hand engineering features.en_US
dc.description.sponsorshipSidi Mohammed Ben Abdellah Univ, Natl Sch Appl Sci, Arabic Language Engn Soc Morocco, Ctr Natl Rech Sci Tech, Acad Hassan II Sci Tech, Ecole Natl Sci Appliquees Fes, LISA, Fac Sci Tech, Fac Sci Schariaen_US
dc.identifier.doi10.1007/978-3-319-73500-9_11en_US
dc.identifier.endpage158en_US
dc.identifier.isbn978-3-319-73500-9; 978-3-319-73499-6
dc.identifier.issn1865-0929en_US
dc.identifier.issn1865-0937en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage147en_US
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-73500-9_11
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36583
dc.identifier.volume782en_US
dc.identifier.wosWOS:000433974200011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofARABIC LANGUAGE PROCESSING: FROM THEORY TO PRACTICEen_US
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArabic languageen_US
dc.subjectArabic sentiment analysisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectPretrained word representationsen_US
dc.titleEmpirical Evaluation of Word Representations on Arabic Sentiment Analysisen_US
dc.typeConference Objecten_US

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