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• W4320060488 abstract "Emotion and mood influence human cognition, memory, judgement, and decision making abilities. The two affective states differ in terms of duration, intensity, and the cause of occurrence. While studies in psychology demonstrate the interactions between mood and emotion, on the contrary, research in the affective computing domain has predominantly focused on emotion inference from various modalities like facial expression, speech, physiological signals, etc., and hardly any studies exist either on mood inference or examining the relation between mood and emotion. In this regard, the objective of this research is to simultaneously model the two affective states even though they might occur on different timelines. By using in-the-wild video data and leveraging the emotion information, the aim of this research is to infer mood and observe the mood-emotion interplay. To this end, the research to date uses AFEW-VA dataset and runs experiments using temporal emotional change in addition to the mood ratings to predict mood. This approach demonstrates that mood prediction is enhanced when emotional change labels are incorporated apart from the mood labels." @default.
• W4320060488 created "2023-02-12" @default.
• W4320060488 creator A5067372958 @default.
• W4320060488 date "2022-11-07" @default.
• W4320060488 modified "2023-09-23" @default.
• W4320060488 title "Mood-Emotion Interplay: A Computational Perspective" @default.
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• W4320060488 doi "https://doi.org/10.1145/3536221.3557027" @default.
• W4320060488 hasPublicationYear "2022" @default.
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