  We consider entity-level sentiment analysis in Arabic, a morphologically rich
language with increasing resources. We present a system that is applied to
complex posts written in response to Arabic newspaper articles. Our goal is to
identify important entity "targets" within the post along with the polarity
expressed about each target. We achieve significant improvements over multiple
baselines, demonstrating that the use of specific morphological representations
improves the performance of identifying both important targets and their
sentiment, and that the use of distributional semantic clusters further boosts
performances for these representations, especially when richer linguistic
resources are not available.
