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The emotion annotation can be done in discrete emotion labels or on a continuous scale. Most of the databases are usually based on the basic emotions theory (by Paul Ekman) which assumes the existence of six discrete basic emotions (anger, fear, disgust, surprise, joy, sadness). However, some databases include the emotion tagging in continuous ...
These measures are broken down into three main categories: basic negative emotion scales consisting of fear, hostility, guilt, and sadness; basic positive emotion scales consisting of joviality, self-assurance, and attentiveness; and other affective states consisting of shyness, fatigue, serenity, and surprise.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context.
The Affective Slider is an empirically validated digital scale for the self-assessment of affect composed of two slider controls that measure basic emotions in terms of pleasure and arousal, [6] which constitute a bidimensional emotional space called core affect, that can be used to map more complex conscious emotional states.
Furthermore, a cross-species analysis of facial expressions can help to answer interesting questions, such as which emotions are uniquely human. [21] The Emotional Facial Action Coding System (EMFACS) [22] and the Facial Action Coding System Affect Interpretation Dictionary (FACSAID) [23] consider only emotion-related facial actions. Examples ...
The response format that is most commonly used in emotion recognition studies is forced choice. In forced choice, for each facial expression, participants are asked to select their response from a short list of emotion labels. The forced choice method determines the emotion attributed to the facial expressions via the labels that are presented ...
The name Differential Emotions Scale came from the examination of verbal labels and facial expressions. Research have shown that participants of different backgrounds (i.e. ethnicity, culture, language) are all able to agree on and can differentiate different facial expressions among the fundamental emotions.
Emotion recognition in conversation (ERC) is a sub-field of emotion recognition, that focuses on mining human emotions from conversations or dialogues having two or more interlocutors. [1] The datasets in this field are usually derived from social platforms that allow free and plenty of samples, often containing multimodal data (i.e., some ...