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.

翻译：尽管大多数情感计算研究专注于推断情绪，但考察心境或理解“心境-情绪相互作用”的关注却显著较少。基于先前研究，我们（a）在不依赖标注标签的情况下，推导并整合情绪变化（Δ）信息以推断心境，以及（b）尝试对长时间视频片段进行心境预测，以契合心境的特性。我们通过预训练孪生网络的度量学习生成情绪变化（Δ）标签，并将其与心境标签一同用于心境分类。实验对比了“单模态”（仅使用心境标签训练）与“多模态”（使用心境加Δ标签训练）模型，结果表明，融入情绪变化信息有益于心境预测，强调了建模心境-情绪相互作用对于有效心境推断的重要性。