Comparative Analysis for Arabic Sentiment Classification Sentiment analysis categorizes human opinions, emotions and reactions extracted from text into positive or negative polarity. However, mining sentiments from the Arabic text is challenging due to the scarcity of Arabic datasets for training the context. To address this gap, this study builds an Arabic sentiment dataset sourced from tweets, product reviews, hotel reviews, movie reviews, product attraction, and restaurant reviews from different websites; manually labeled for training the sentiment analysis model. The dataset is then used in a comparative experiment with three machine learning algorithms, which are Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT) via a classification methodology. The best results for polarity prediction in sentiment analysis models was achieved by SVM with product attraction dataset, with the accuracy of 0.96, precision of 0.99, recall of 0.99, and Fmeasure of 0.98. This is followed by the average performance from NB and DT. It can be concluded that the ML classifiers need the right morphological features to enhance the classification accuracy when dealing with different words that play different roles in the sentence with the same letters.

Arabic Sentiment Analysis: A Systematic Literature Review With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchersxe2x80x99 search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.