Due to the implicit traits embedded in the language, handling figurative languages appear to be the most trending topics in
public opinion mining and social multimedia sentiment analysis. Failures in recognition of these languages can lead to the
misrepresentation of actual sentiments, attitudes or opinions person or community try to expose. Satire is a more alive form of figurative
communication which intends to criticize someone's behavior and ridicule it. This work proposes the POS level emotion-based features
by using the emotion lexicon SenticNet and VADER. It is approached as a classification problem by applying a supervised machine
learning algorithm: Random Forest. The system can tackle the problem of high bias error in both long text and short text datasets with
83% to 89% accuracy whereas the BOW gives high accuracy but cannot handle the problem of high bias error in satirical language
Published In:IJCSN Journal Volume 7, Issue 6
Date of Publication : December 2018
Pages : 374-381
Tables : 07
Pyae Phyo Thu :
University of Computer Studies, Mandalay
Than Nwe Aung :
University of Computer Studies, Mandalay
Satire Detection, POS Level Features, Emotion-Based Features, SenticNet, VADER
In conclusion, an approach to detect satirical figurative
language from an emotional point of view has proposed. It
contributes in two ways: (1) although the ironic dimension
of the language causes difficulty in the detection of satire
from emotion, emotion-based satirical language processing
is proposed using the emotion lexicon SenticNet and
VADER. (2) Emotions in figurative languages are
ambiguous and often lead to high bias errors, but a series
of experiments are performed to prove that the proposed
POS level emotion-based features can tackle to problem of
ambiguous nature of emotion.
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