A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
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Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the 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 unimodal (training only using mood labels) vs 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.
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