Emotions are central for a wide range of everyday human experiences and understanding emotions is a key problem both in the business world and in the fields of physiology and neuroscience. The most well-known theory of emotions proposes a categorical systemof emotion classification, where emotions are classified as discrete entities, while psychologists say that in general man will hardly express a single basic emotion. According to this observation, alternative models have been developed, which define multiple dimensions corresponding to various parameters and specify emotions along those dimensions. Recently, one of the most used models in affective computing is the Lovheim’s cube of emotions, i.e., a theoretical model that focuses on the interactions of monoamine neurotransmitters and emotions. This work presents a comparison between a single automatic classifier able to recognize the basic emotions proposed in the Lovheim’s cube and a set of independent binary classifiers, each one able to recognize a single dimension of the Lovehim’s cube. The application of this model has determined a notable improvement of results: in fact, in the best case there is an increment of the accuracy of 11,8%. The set of classifiers has been modeled and deployed on the distributed ActoDeS application architecture. This implementation improves the computational performance and it eases the system reconfiguration and its ability to recognize particular situations, consisting of particular combinations of basic emotions.
|Titolo:||Application of Lovheim model for emotion detection in english tweets|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1b Atto convegno Volume|