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Recognition of Emotion with SVMs

Authors: Zhi Teng; Fuji Ren; Shingo Kuroiwa;

Recognition of Emotion with SVMs

Abstract

In recent years, several methods on human emotion recognition have been published. In this paper, we proposed a scheme that applied the emotion classification technique for emotion recognition. The emotion classification model is Support Vector Machines (SVMs). The SVMs have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. The Emotion Recognition System will be recognise emotion from the sentence that was inputted from the keyboard. The training set and testing set were constructed to verify the effect of this model. Experiments showed that this method could achieve better results in practice. The result showed that this method has potential in the emotion recognition field.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Top 10%
Average
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