Sentence examples for classification of emotion from inspiring English sources

Valence judgment (or sentiment classification) of emotion words and emotion-laden words has been carried out in both lines of emotion studies, by psycholinguists and computer scientists, respectively.

In general, these data vocabulary can be letters, words, syllables, and so on, and we use N-gram model to implement the automatic classification of emotion.

It should be noted that Lee's model relies on an assumption that the classification of emotion words (by emotion type and intensity) can be transferred across languages via translation equivalents.

The channels having the highest performance were selected for classification of emotions.

The comparison of averaged values for classification of emotions is shown in Fig. 10.

It was concluded that MLPNN and kNN used in this study give good accuracy results for classification of emotions.

For classification of emotions, DWT was used as feature extraction, while MLPNN and kNN methods were used as classifiers.

Another issue is to find the optimum number of parameters in the feature vector for robust GMM classification of emotions.

The same procedure that was applied for channel selection was employed for the classification of emotions as well.

On the other hand, since the short-term speech energy is closely related with activation or arousal dimension of the emotion, its usage in the conventional features contributes to the classification of emotions which have similar arousal level [20].

Open image in new window Fig. 10 Overall performances of MLPNN and kNN classifiers   (f) It was concluded that MLPNN and kNN used in this study give good accuracy results for classification of emotions.  .