Sentence examples for class of emotions from inspiring English sources

According to forms of sentimentalism that limit their FA theory to a core class of emotions, sensibility theory exaggerates the interdependence of these responses (such as fear, amusement, disgust, shame, and the like) with their associated value properties (the fearsome, funny, disgusting, shameful, etc).

There are at least four main modifications involving Brentano's view of mental phenomena, especially Brentano's tripartite classification of such phenomena into representations, judgments, and phenomena of love and hate (or emotions [Gemütsbewegungen]): (1) Meinong's first deviation consists in dividing up the class of emotions into the classes of feelings [Gefühle] and of desires [Begehrungen].

B. Titchener, 1908, 1927; R. Reisenzein and W. Schönpflug, 1992; D. Fisette, 2013) At issue was whether the nature of pleasure provided by an object such as a work of art, for instance, is intentional, as was the case in Brentano's theory wherein it is closely related to the class of emotions, or phenomenal as in Husserl and the sensualist theories of James and Mach.

Once the classifier has been trained, it is used to assign input features to a particular class of emotion.

Figure 14 Influence of limited length of feature vector on stability of the GMM emotion classification process; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature sets P3_8, P3_12, and P3_16, N gmix = 6, N iter = 1200; tested sentence expressed by the female speaker in joyous style.

Figure 15 Influence of incorrectly chosen GMM model of gender type on stability of the emotion classification; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed in neutral speaking style by the male speaker (left two graphs), and by the female speaker (right graphs).

Figure 16 Stability test of the GMM emotion classifier for male gender; obtained scores (upper set of graphs), and finally determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed by the male speaker in neutral and emotional styles.

Figure 17 Stability test of the GMM emotion classifier for female gender; obtained scores (upper set of graphs), and finally determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed by the female speaker in neutral and emotional styles.

Both classes of emotions first require us to infer what a person's life as a whole must be like.

The creation of a new multi-label dataset with 72 music features for 593 songs categorized into one or more out of 6 classes of emotions.

All samples were assessed by a large group of human evaluators (experts and volunteers) and labeled into the abovementioned classes of emotions, summarized in Table 1.