  Affective video indexing is the area of research that develops techniques to
automatically generate descriptions of video content that encode the emotional
reactions which the video content evokes in viewers. This paper provides a set
of corpus development guidelines based on state-of-the-art practice intended to
support researchers in this field. Affective descriptions can be used for video
search and browsing systems offering users affective perspectives. The paper is
motivated by the observation that affective video indexing has yet to fully
profit from the standard corpora (data sets) that have benefited conventional
forms of video indexing. Affective video indexing faces unique challenges,
since viewer-reported affective reactions are difficult to assess. Moreover
affect assessment efforts must be carefully designed in order to both cover the
types of affective responses that video content evokes in viewers and also
capture the stable and consistent aspects of these responses. We first present
background information on affect and multimedia and related work on affective
multimedia indexing, including existing corpora. Three dimensions emerge as
critical for affective video corpora, and form the basis for our proposed
guidelines: the context of viewer response, personal variation among viewers,
and the effectiveness and efficiency of corpus creation. Finally, we present
examples of three recent corpora and discuss how these corpora make progressive
steps towards fulfilling the guidelines.
