Whilst a majority of affective computing research focuses on inferring
emotions, examining mood or understanding the \textit{mood-emotion interplay}
has received significantly less attention. Building on prior work, we (a)
deduce and incorporate emotion-change (Δ) information for inferring
mood, without resorting to annotated labels, and (b) attempt mood prediction
for long duration video clips, in alignment with the characterisation of mood.
We generate the emotion-change (Δ) labels via metric learning from a
pre-trained Siamese Network, and use these in addition to mood labels for mood
classification. Experiments evaluating \textit{unimodal} (training only using
mood labels) vs \textit{multimodal} (training using mood plus Δ labels)
models show that mood prediction benefits from the incorporation of
emotion-change information, emphasising the importance of modelling the
mood-emotion interplay for effective mood inference.Comment: 9 pages, 3 figures, 6 tables, published in IEEE International
Conference on Affective Computing and Intelligent Interactio