A cross-study of Sentiment Classification on Arabic corpora

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 Naive 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 Naive Bayes and Support Vector Machines are competitively effective; however k- Nearest Neighbor’s effectiveness depends on the corpus. Through this 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.