  Social media reflects the public attitudes towards specific events. Events
are often related to persons, locations or organizations, the so-called Named
Entities. This can define Named Entities as sentiment-bearing components. In
this paper, we dive beyond Named Entities recognition to the exploitation of
sentiment-annotated Named Entities in Arabic sentiment analysis. Therefore, we
develop an algorithm to detect the sentiment of Named Entities based on the
majority of attitudes towards them. This enabled tagging Named Entities with
proper tags and, thus, including them in a sentiment analysis framework of two
models: supervised and lexicon-based. Both models were applied on datasets of
multi-dialectal content. The results revealed that Named Entities have no
considerable impact on the supervised model, while employing them in the
lexicon-based model improved the classification performance and outperformed
most of the baseline systems.
