  In this paper, we introduce a set of opinion annotations for the POM movie
review dataset, composed of 1000 videos. The annotation campaign is motivated
by the development of a hierarchical opinion prediction framework allowing one
to predict the different components of the opinions (e.g. polarity and aspect)
and to identify the corresponding textual spans. The resulting annotations have
been gathered at two granularity levels: a coarse one (opinionated span) and a
finer one (span of opinion components). We introduce specific categories in
order to make the annotation of opinions easier for movie reviews. For example,
some categories allow the discovery of user recommendation and preference in
movie reviews. We provide a quantitative analysis of the annotations and report
the inter-annotator agreement under the different levels of granularity. We
provide thus the first set of ground-truth annotations which can be used for
the task of fine-grained multimodal opinion prediction. We provide an analysis
of the data gathered through an inter-annotator study and show that a linear
structured predictor learns meaningful features even for the prediction of
scarce labels. Both the annotations and the baseline system are made publicly
available. https://github.com/eusip/POM/
