A
Concept-based Sentiment Analysis Approach for Arabic
Ahmed Nasser1 and Hayri
Sever2
1Control
and Systems Engineering Department, University of Technology, Iraq
2Department of Computer Engineering, Çankaya University, Etimesgut
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.
Keywords: Arabic Sentiment Analysis, Concept-based Sentiment Analysis,
Machine Learning and Ensemble Learning.
Received
December13, 2017; accepted July 29, 2019