Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms

Document sentiment classification is of-ten processed by applying machine learn-ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train-ing the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Ara-bic sentiment analysis by combining dif-ferent types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investi-gate its contribution in Arabic sentiment classification. Obtained results attained 85.06 % in terms of macro-averaged F-measure, and showed that discourse fea-tures have moderately improved F-measure by approximately 3 % or 4%.