In a world of social media, sentiment analysis has played a significant role in gathering useful trends and information on mass opinion towards any individual, product, organization, political group or any sports franchise. Sarcasm is one such unique sentiment, where the intended meaning is the opposite of written text (opinion). It can also be defined as concealed mockery through written or expressed remark which makes it complicated in sentiment detection systems. Numerical sarcasm is one such field that attracted researchers. Finding the sarcasm due to the presence of numerical data in the given statement can be concluded as numerical sarcasm detection. There are various computational systems in this paper that we tried to incorporate with Machine Learning and Deep learning approaches. We have used techniques such as SVM, K-NN, and LSTM for numerical sarcasm detection and incorporated sci-kit, numpy, tensor flow in our proposed work.

Sentimental Analysis, Social Media, Machine Learning, DNN, Semantic features

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