Abstract
Target identification is one of the important tasks related to opinion mining. Indeed, there are few works in this field that deals with Arabic Language because of the lack of annotated corpora. In this paper, we propose to investigate the problem of opinion target identification from Arabic news articles using Conditional Random Fields (CRF) as discriminative framework. Opinion target recognition task consists in determining terms forming the target span. To the best of our knowledge, there is no similar work done in this field for Arabic language and especially for news articles. Experiments show that we can perform excellent results with consideration of semantic correlation between words and without relying on deep syntactic features. Our proposed method identifies opinion target with 95% F-measure, for a given opinion word using bi-gram feature, words in context and other features.