  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 ($\Delta$) 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 ($\Delta$) 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 $\Delta$ 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.
