Crowdsourcing for Affective Annotation of Video: Development of a Viewer-reported Boredom Corpus

Abstract

Predictions of viewer affective response to video are an important source of information that can be used to enhance the performance of multimedia retrieval and recommendation systems. The development of algorithms for robust prediction of viewer affective response requires corpora accompanied by appropriate ground truth. We report on the development a new corpus to be used to evaluate algorithms for prediction of viewer-reported boredom.We make use of crowdsourcing in order to address two shortcomingsof previous affective video corpora: small number of annotators and gap between annotators and target viewer group. We describe the design of the Mechanical Turk setup that we used to generate the affective annotations for the corpus. We discuss specific issues that arose and how we resolve them and then present an analysis of the annotations collected. The paper closes with a list of recommended practices for the collection of self-reported affective annotations using crowdsourcing techniques and an outlookon future work.

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