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  Developing POS Level Emotion-Based Features on Satire Detection  
  Authors : Pyae Phyo Thu; Than Nwe Aung
  Cite as:


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 processing.


Published In : IJCSN Journal Volume 7, Issue 6

Date of Publication : December 2018

Pages : 374-381

Figures :03

Tables : 07


Pyae Phyo Thu : University of Computer Studies, Mandalay Mandalay, Myanmar

Than Nwe Aung : University of Computer Studies, Mandalay Mandalay, Myanmar


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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