  How could we gather affect annotations in a rapid, unobtrusive, and
accessible fashion? How could we still make sure that these annotations are
reliable enough for data-hungry affect modelling methods? This paper addresses
these questions by introducing PAGAN, an accessible, general-purpose, online
platform for crowdsourcing affect labels in videos. The design of PAGAN
overcomes the accessibility limitations of existing annotation tools, which
often require advanced technical skills or even the on-site involvement of the
researcher. Such limitations often yield affective corpora that are restricted
in size, scope and use, as the applicability of modern data-demanding machine
learning methods is rather limited. The description of PAGAN is accompanied by
an exploratory study which compares the reliability of three continuous
annotation tools currently supported by the platform. Our key results reveal
higher inter-rater agreement when annotation traces are processed in a relative
manner and collected via unbounded labelling.
