Abstract
This paper presents an approach of fine-grained opinion categorization in Arabic news articles. This approach is based on lexical semantic analysis. We propose to categorize every opinion expression using a proposed typology of four top-level semantic categories: reporting, judgment, advice and sentiment. Each word or opinion expression will be annotated with a semantic representation which takes in consideration specificities of Arabic language. To the best of our knowledge, there is no annotated Arabic opinion corpus with the proposed semantic representation. The task of categorization is considered as a classification problem. So, we use a Conditional Random Fields (CRF) as a discriminative model that we consider as a good contribution, because of the lack of similar fine-grained opinion categorization performed with CRF. The obtained results show that the integration of CRF models is important for opinion classification of the Arabic language.