Analysis on Emotion Classification Methods
Abstract
Emotional intelligence is the
ability to understand changing states of
emotion, it is an important aspect of human
interaction. With upcoming developments
emotion identification is an important aspect
in HCI. Ideally if a computer can identify a
human’s emotions and respond to it
accordingly human computer interactions
would be much more natural and more
convenient. But even from a human’s
perspective emotions are hard to identify
and track, hence for a computer to identify
accurate emotions can be challenging.
Nonetheless there exists few methods to
classify and label emotions into categories.
Hence this research is an analysis of methods
used to classify emotions. Discussing the
strengths and weaknesses in communication
cues such as facial expression classifiers,
gesture movements, acoustic emotion
classifiers and emotion mining in text. It
argues that there exists an increment of
accuracy when two or more systems are
paired to extract the features in different
situations. Hence results show that, while
each model has its advantages and
disadvantages, when integrated to classify, it
gives better, more accurate prediction and
improved results. Additionally, this paper
mentions some of the practical issues that
exist when it comes to emotion recognition
and HCI. Furthermore, it is identified that
emotion identification via text is a research
area which holds great potential and among
many approaches hand crafted models with
the use of machine learning gives the best
results. Finally, it proposes a solution, a
mobile application for emotional support
using emotion identification via text
messages.
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