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Opinion Question Answering (Opinion QA) is the task of enabling users to explore others opinions towar d a particular service of product in order to make deci sions. Arabic Opinion QA is more challenging due to its complex morphology compared to other languages and has many varieties dialects. On the other hand, the re are insignificant research efforts and resources availa ble that focus on Opinion QA in Arabic. This study aims to address the difficulties of Arabic opinion QA by pr oposing a hybrid method of lexicon-based approach and classification using Naive Bayes classifier. The pr oposed method contains pre-processing phases such as, transformation, normalization and tokenization and exploiting auxiliary information (thesaurus). The l exiconbased approach is executed by replacing some words with its synonyms using the domain dictionary. The classification task is performed by Naive Bayes cla ssifier to classify the opinions based on the posit ive or negative sentiment polarity. The proposed method has been evaluated using the common information retri eval metrics i.e., Precision, Recall and F-measure. For comparison, three classifiers have been applied whi ch are Naive 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.

A HYBRID METHOD USING LEXICON-BASED APPROACH AND NAIVE BAYES CLASSIFIER FOR ARABIC OPINION QUESTION ANSWERING