'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Representing computationally everyday emotional
states is a challenging task and, arguably, one of the most fundamental
for affective computing. Standard practice in emotion annotation
is to ask humans to assign an absolute value of intensity
to each emotional behavior they observe. Psychological theories
and evidence from multiple disciplines including neuroscience,
economics and artificial intelligence, however, suggest that the
task of assigning reference-based (relative) values to subjective
notions is better aligned with the underlying representations
than assigning absolute values. Evidence also shows that we
use reference points, or else anchors, against which we evaluate
values such as the emotional state of a stimulus; suggesting
again that ordinal labels are a more suitable way to represent
emotions. This paper draws together the theoretical reasons to
favor relative over absolute labels for representing and annotating
emotion, reviewing the literature across several disciplines. We
go on to discuss good and bad practices of treating ordinal
and other forms of annotation data, and make the case for
preference learning methods as the appropriate approach for
treating ordinal labels. We finally discuss the advantages of
relative annotation with respect to both reliability and validity
through a number of case studies in affective computing, and
address common objections to the use of ordinal data. Overall,
the thesis that emotions are by nature relative is supported by
both theoretical arguments and evidence, and opens new horizons
for the way emotions are viewed, represented and analyzed
computationally.peer-reviewe