A cross-study of Sentiment Classification on Arabic corpora A. Mountassir 1 , H. Benbrahim 2 and I. Berrada 3 Abstract Sentiment Analysis is a research area where the studies focus on processing and analyzing the opinions available on the web. Several interesting and advanced works were performed on English. In contrast, very few works were conducted on Arabic. This paper presents the study we have carried out to investigate supervised sentiment classification in an Arabic context. We use two Arabic Corpora which are different in many aspects. We use three common classifiers known by their effectiveness, namely Naïve Bayes, Support Vector Machines and k-Nearest Neighbor. We investigate some settings to identify those that allow achieving the best results. These settings are about stemming type, term frequency thresholding, term weighting and n-gram words. We show that Naïve Bayes and Support Vector Machines are competitively effective; however k- Nearest Neighbor’s effectiveness depends on the corpus. Through t his study, we recommend to use light-stemming rather than stemming, to remove terms that occur once, to combine unigram and bigram words and to use presence-based weighting rather than frequency-based one. Our results show also that classification performance may be influenced by documents length, documents homogeneity and the nature of document authors. However, the size of data sets does not have an impact on classification results. 1 Introduction Nowadays, the web is no longer just a source of information for internet users; it represents also a space where simple users can provide information. With the emergence of social media (such as social networking sites, online news sites, online web forums, personal blogs and online review sites), internet users are more and more invited to express their opinions, post comments or share experiences about any topic. Therefore, the online opinion has become an 1 ENSIAS, Mohamed 5 University, Rabat, Morocco asmaa.mountassir@gmail.com 2 ENSIAS, Mohamed 5 University, Rabat, Morocco benbrahim@ensias.ma 3 ENSIAS, Mohamed 5 University, Rabat, Morocco iberrada@ensias.ma