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.

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[36] Wille R., “Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts,” in Proceedings of International Conference on Formal Concept Analysis, Berlin, pp. 445-470, 1982. Ahmed Nasser got his BSc degree from University of Technology Control and Systems Eng. Faculty, Baghdad Iraq in 2006, MSc degree in Computer Eng. from Istanbul University, Istanbul Turkey in 2012, PhD degree from Hacettepe University, Ankara Turkey Computer Eng., in 2018. His current interest on “Data Mining”, “Natural Language Processing” and “Machine Learning”. Hayri Sever is currently working for Software Engineering Department atCankaya University. He has received his BSc degree in computer science andengineering from Hacettepe University in Ankara, TR, MSc degree from MaineUniversity in Orono, ME in 1991, PhD degree from Louisiana University inLafayette LA, in 1995. His research areas are Knowledge Discovery in Databases,Multimedia Retrieval Models and Systems, Multimedia Systems, UncertaintyReasoning, Business Process Management, Machine Learning, and Speech Analysis.