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Emotion classification, the means by which one may distinguish or contrast one emotion from another, is a contested issue in emotion research and in affective science. Researchers have approached the classification of emotions from one of two fundamental viewpoints: [citation needed] that emotions are discrete and fundamentally different constructs
Emotion-Driven Outcomes: AET posits that emotions generated by affective events at work have consequences for employee attitudes and behaviors. For example, positive ...
The Blob Tree collection consists of a set of illustrations of blob figures in various poses and expressions, each representing a different emotion or feeling. [4] These illustrations are intended to be used as prompts for individuals to identify and express their own emotions, or as a way to start a conversation about emotions and feelings. [5]
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A facial expression database is a collection of images or video clips with facial expressions of a range of emotions.Well-annotated (emotion-tagged) media content of facial behavior is essential for training, testing, and validation of algorithms for the development of expression recognition systems.
Emotional granularity is an individual's ability to differentiate between the specificity of their emotions. Similar to how an interior decorator is aware of fine gradations in shades of blue, where others might see a single color, [1] an individual with high emotional granularity would be able to discriminate between their emotions that all fall within the same level of valence and arousal ...
Stanley Schachter and Jerome Singer proposed a theory also known as the two-factor theory of emotion, which implies emotion have two factors: physical arousal and cognitive label. This suggests that if the physiological activity occurs first, then it must cognitively be distinguished as the cause of the arousal and labeled as an emotion.