چکیده (انگلیسی):

Opinion Question Answering (Opinion QA) is the task of enabling users to explore others opinions toward a
particular service of product in order to make decisions. Arabic Opinion QA is more challenging due to its
complex morphology compared to other languages and has many varieties dialects. On the other hand, there are
insignificant research efforts and resources available that focus on Opinion QA in Arabic. This study aims to
address the difficulties of Arabic opinion QA by proposing a hybrid method of lexicon-based approach and
classification using Naïve Bayes classifier. The proposed method contains pre-processing phases such as,
transformation, normalization and tokenization and exploiting auxiliary information (thesaurus). The lexiconbased
approach is executed by replacing some words with its synonyms using the domain dictionary. The
classification task is performed by Naïve Bayes classifier to classify the opinions based on the positive or
negative sentiment polarity. The proposed method has been evaluated using the common information retrieval
metrics i.e., Precision, Recall and F-measure. For comparison, three classifiers have been applied which are
Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The experimental results
have demonstrated that NB outperforms SVM and KNN by achieving 91% accuracy.