  While emotion and mood interchangeably used, they differ in terms of
duration, intensity and attributes. Even as multiple psychology studies examine
the mood-emotion relationship, mood prediction has barely been studied. Recent
machine learning advances such as the attention mechanism to focus on salient
parts of the input data, have only been applied to infer emotions rather than
mood. We perform mood prediction by incorporating both mood and emotion change
information. We additionally explore spatial and temporal attention, and
parallel/sequential arrangements of the spatial and temporal attention modules
to improve mood prediction performance. To examine generalizability of the
proposed method, we evaluate models trained on the AFEW dataset with EMMA.
Experiments reveal that (a) emotion change information is inherently beneficial
to mood prediction, and (b) prediction performance improves with the
integration of sequential and parallel spatial-temporal attention modules.
