A multimodal movie review corpus for fine-grained opinion mining
This provides a dataset for fine-grained opinion mining in multimodal contexts, but it is incremental as it builds on existing datasets.
The authors introduced a new multimodal movie review corpus with fine-grained opinion annotations, including polarity and aspects, and demonstrated that a linear structured predictor can learn meaningful features for scarce labels.
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/