This paper deals with a multimodal annotation scheme dedicated to the study of gestures in interpersonal communication, with particular regard to the role played by multimodal expressions for feedback, turn management and sequencing. The scheme has been developed under the framework of the MUMIN network and tested on the analysis of multimodal behaviour in short video clips in Swedish, Finnish and Danish. The preliminary results obtained in these studies show that the reliability of the categories defined in the scheme is acceptable, and that the scheme as a whole constitutes a versatile analysis tool for the study of multimodal communication behaviour.

In this paper we present an annotated corpus created with the aim of analyzing the informative behaviour of emoji -an issue of importance for sentiment analysis and natural language processing. The corpus consists of 2475 tweets all containing at least one emoji, which has been annotated using one of the three possible classes: Redundant, Non Redundant, and Non Redundant + POS. We explain how the corpus was collected, describe the annotation procedure and the interface developed for the task. We provide an analysis of the corpus, considering also possible predictive features, discuss the problematic aspects of the annotation, and suggest future improvements.

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