Ensemble of Classification Algorithms for Subjectivity and Sentiment Analysis of Arabic Customers' Reviews

Sentiment Analysis is a very challenging and important task that contains natural language processing, web mining and machine learning. Up to date, few researches have been conducted on sentiment classification for Arabic languages due to the lack of resources for managing sentiments or opinions such as senti-lexicons and opinion corpora. The main obstacle in Arabic sentiment analysis is that phrases and words that are used by Arabic web users to express sentiments are highly subjected to usage trends. In addition, the use of dialectal phrases and words contributes to ambiguity in the analysis of Arabic sentiments and opinions. To antidote this shortage, this study proposes an ensemble of machine learning classifiers framework for handling the problem of subjectivity and sentiment analysis for Arabic customer reviews. First of all, three renowned text classification algorithms, called Naive Bayes, Rocchio classifier and support vector machines, are adopted as base-classifiers. Second, we make a comparative study of two kinds of ensemble methods, namely the fixed combination and meta-classifier combination. The experimental results show that the ensemble of the classifiers improves the classification effectiveness in terms of macro-F1 for both levels. The best results obtained for the subjectivity analysis and the sentiment classification in terms of macro-F1 are 97.13% and 90.95% respectively.