Abstract
It is becoming a trend in current society to use complex and indirect statements for communication which includes metaphorical language, proverbs and other similar forms. One of such communication form is sarcasm. Sarcastic statements can have symbolized, hidden or even entirely opposite meaning from the conveyed statement. Sarcasm is inherently ambiguous in nature which makes it very difficult to understand even for humans let alone machines. In this paper, we have implemented sarcasm detection based upon difference and similarity between facial emotion of the person and sentiment of his verbally conveyed message.
Highlights
Sarcasm is a “sharp, bitter, or cutting expression or remark; a bitter gibe or taunt” that mean the opposite of what they say, made to criticize someone or something in a way that is amusing to others but annoying to the person criticized It might employ ambivalence, sarcasm is not necessarily ironic
Facial emotion will be obtained by real time face recognition and emotion analysis system
The dataset used for facial emotion recognition is fer2013 dataset published on International Conference on Machine Learning (ICML)
Summary
Sarcasm is a “sharp, bitter, or cutting expression or remark; a bitter gibe or taunt” that mean the opposite of what they say, made to criticize someone or something in a way that is amusing to others but annoying to the person criticized It might employ ambivalence, sarcasm is not necessarily ironic. Carlos Busso, et al[5] analyzed the strengths and weaknesses of facial expression classifiers and acoustic emotion classifiers In this paper, they have compared many emotion recognition systems to see which system performs better. The muscles of the face can be changed and the tone and the energy in the production of the speech can be intentionally modified to communicate different feelings which make it difficult to detect emotion from facial expressions or tone. To detect facial expressions the features used are based on local spatial position or displacement of specific points and regions of the face. They proposed an emotion recognition system, in. With the use of this dictionary approach they achieved 88% accuracy in classification of six basic emotions
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