  Emotionally annotated databases are repositories of multimedia documents with
annotated affective content that elicit emotional responses in exposed human
subjects. They are primarily used in research of human emotions, attention and
development of stress-related mental disorders. This can be successfully
exploited in larger processes like selection, evaluation and training of
personnel for occupations involving high stress levels. Emotionally annotated
databases are also used in multimodal affective user interfaces to facilitate
richer and more intuitive human-computer interaction. Multimedia documents in
emotionally annotated databases must have maximum personal ego relevance to be
the most effective in all these applications. For this reason flexible
construction of subject-specific of emotionally annotated databases is
imperative. But current construction process is lengthy and labor intensive
because it inherently includes an elaborate tagging experiment involving a team
of human experts. This is unacceptable since the creation of new databases or
modification of the existing ones becomes slow and difficult. We identify a
positive correlation between the affect and semantics in the existing
emotionally annotated databases and propose to exploit this feature with an
interactive relevance feedback for a more efficient construction of emotionally
annotated databases. Automatic estimation of affective annotations from
existing semantics enhanced with information refinement processes may lead to
an efficient construction of high-quality emotionally annotated databases.
