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. this https URL

本文介绍了针对 POM 电影评论数据集的一组观点注释，包括两个粒度级别的粗略层面和细粒度层面，并提出了一些具有特定类别的注释以便于电影评论的观点注释。我们还提供了一个可以预测意见（例如极性和方面）不同组成部分的分层观点预测框架，并提供了第一组可用于细粒度多模态观点预测任务的地面事实注释。此外，我们进行了一项相关数据采集的研究，并在不同层面的粒度下报告了标注者间的一致性。最后，我们提供了一个基准系统和注释数据以供公开使用。