  Successful management of emotional stimuli is a pivotal issue concerning
Affective Computing (AC) and the related research. As a subfield of Artificial
Intelligence, AC is concerned not only with the design of computer systems and
the accompanying hardware that can recognize, interpret, and process human
emotions, but also with the development of systems that can trigger human
emotional response in an ordered and controlled manner. This requires the
maximum attainable precision and efficiency in the extraction of data from
emotionally annotated databases While these databases do use keywords or tags
for description of the semantic content, they do not provide either the
necessary flexibility or leverage needed to efficiently extract the pertinent
emotional content. Therefore, to this extent we propose an introduction of
ontologies as a new paradigm for description of emotionally annotated data. The
ability to select and sequence data based on their semantic attributes is vital
for any study involving metadata, semantics and ontological sorting like the
Semantic Web or the Social Semantic Desktop, and the approach described in the
paper facilitates reuse in these areas as well.
