Towards Arabic semantic opinion mining
Imen Touati, Marwa Graja, Mariem Ellouze, Lamia Hadrich Belguith · 2016
Arabic opinion mining is a challenging task because Arabic is morphologically and semantically rich language. In this paper, we are interested in analyzing opinions in Arabic news articles. We propose to use a machine learning technique to classify opinions or sentiments at the expression level. Our approach involves determining the semantic category of the expression. It also includes the classification of the opinion expression into positive or negative and the classification of its intensity into high, medium and low. Our method relies on wide range of features which are used in the literature like n-grams, morphological, stylistic features, etc. In addition, we propose new features inspired from contextual, semantic information and others specific for Arabic language. In the same context, we try to have a good contribution in opinion mining in Arabic by proposing to use Conditional Random Fields as a discriminative model. We carry out many experiments by combining at the same time different set of features to find the best combination that yield the best results. We evaluate our method at the expression level using a corpus of Arabic news articles. Our method achieves a good result that reaches 84.93% for contextual polarity classification and 87.54% for semantic opinion expression categorization.