Corpus Development for Affective Video Indexing
Mohammad Soleymani, Martha Larson, Thierry Pun, Alan Hanjalic

TL;DR
This paper offers guidelines for developing affective video corpora to improve emotion-based video indexing, addressing challenges in capturing viewer reactions and ensuring data consistency.
Contribution
It introduces a set of standardized guidelines for creating affective video datasets, enhancing consistency and effectiveness in affective multimedia research.
Findings
Three critical dimensions for affective corpora identified
Analysis of three recent corpora demonstrating guideline application
Guidelines support more reliable affective video indexing
Abstract
Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of corpus development guidelines based on state-of-the-art practice intended to support researchers in this field. Affective descriptions can be used for video search and browsing systems offering users affective perspectives. The paper is motivated by the observation that affective video indexing has yet to fully profit from the standard corpora (data sets) that have benefited conventional forms of video indexing. Affective video indexing faces unique challenges, since viewer-reported affective reactions are difficult to assess. Moreover affect assessment efforts must be carefully designed in order to both cover the types of affective responses that video…
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