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A Concept-based Sentiment Analysis Approach for Arabic

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dc.contributor.author Nasser, Ahmed
dc.contributor.author Sever, Hayri
dc.date.accessioned 2021-06-17T11:51:02Z
dc.date.available 2021-06-17T11:51:02Z
dc.date.issued 2020-09
dc.identifier.citation Nasser, Ahmed; Sever, Hayri (2020). "A Concept-based Sentiment Analysis Approach for Arabic", The International Arab Journal of Information Technology, Vol. 17, No. 5, pp. 778-788. tr_TR
dc.identifier.issn 1683-3198
dc.identifier.uri http://hdl.handle.net/20.500.12416/4828
dc.description.abstract Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features + Lexicon-Based-Features + Word2vec-Features (CBF + LEX+ W2V) features combinations. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.34028/iajit/17/5/11 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Arabic Sentiment Analysis tr_TR
dc.subject Concept-Based Sentiment Analysis tr_TR
dc.subject Machine Learning and Ensemble Learning tr_TR
dc.title A Concept-based Sentiment Analysis Approach for Arabic tr_TR
dc.type article tr_TR
dc.relation.journal The International Arab Journal of Information Technology tr_TR
dc.contributor.authorID 11916 tr_TR
dc.identifier.volume 17 tr_TR
dc.identifier.issue 5 tr_TR
dc.identifier.startpage 778 tr_TR
dc.identifier.endpage 788 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü tr_TR


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