  We propose a formal definition for the task of suggestion mining in the
context of a wide range of open domain applications. Human perception of the
term \emph{suggestion} is subjective and this effects the preparation of hand
labeled datasets for the task of suggestion mining. Existing work either lacks
a formal problem definition and annotation procedure, or provides domain and
application specific definitions. Moreover, many previously used manually
labeled datasets remain proprietary. We first present an annotation study, and
based on our observations propose a formal task definition and annotation
procedure for creating benchmark datasets for suggestion mining. With this
study, we also provide publicly available labeled datasets for suggestion
mining in multiple domains.
