Sarcasm Detection using Cognitive Features of Visual Data by Learning Model

Abstract

The objective of the paper is to detect sarcasm in human communication. The methodology uses basic cognitive features of human utterances by capturing three modes of data viz., voice, text, and temporal facial features. The captured data is unstructured as it consists of parameters of feelings and emotions to generate sarcasm which affects expressions through glottal and facial organs. The data capturing method is equally challenging as compared to the method of data processing to acquire features. The significant work is aligned to make natural decisions in the prediction processes using cognitive information in the data lineage. Sarcasm detection in natural human communication is a challenging process. The Linguistic features of natural language processing (NLP) methods help identify sentiment as negative and positive sentences based on polarity using the pre-labelled samples. The multiclass neural network model is used as a soft cognition method for the detection of sarcasm under cloud resources. Identified cognitive features have information like voice cues and eye movements, they tend to influence the decision of detecting sarcasm. The visual data are found to be quite interesting and can establish a strong platform in the area of NLP for further research work.