  A challenge of managing and extracting useful knowledge from social media
data sources has attracted much attention from academic and industry. To
address this challenge, semantic analysis of textual data is focused in this
paper. We propose an ontology-based approach to extract semantics of textual
data and define the domain of data. In other words, we semantically analyse the
social data at two levels i.e. the entity level and the domain level. We have
chosen Twitter as a social channel challenge for a purpose of concept proof.
Domain knowledge is captured in ontologies which are then used to enrich the
semantics of tweets provided with specific semantic conceptual representation
of entities that appear in the tweets. Case studies are used to demonstrate
this approach. We experiment and evaluate our proposed approach with a public
dataset collected from Twitter and from the politics domain. The ontology-based
approach leverages entity extraction and concept mappings in terms of quantity
and accuracy of concept identification.
