A novel mathematical modeling in shift in emotion for gauging the social influential in big data streams with hybrid sarcasm detection

Abstract

Online social network is a platform that plays an essential role in identifying the emotional values of user-generated content such as blogs, posts, and comments along with their influential factors. Especially on Twitter, network users are growing worldwide day by day and creating a massive amount of data that is not analyzed effectively in a quick way. Identifying the most influential persons on the social network is also a challenging task over the wide range of real-time applications like recommendation systems. Now, to handle these situations, this article proposes a novel approach for prediction of information diffusion that includes emotion recognition with sarcasm detection based influence spreader identification (PID-ERSDISI). The proposed method uses the user-generated posts for emotion recognition in tandem with sarcasm detection both implicitly and explicitly. This approach helps to gauge the leverage that influences spreaders and also enhances the prediction accuracy of information diffusion in a better way. The implementation of the proposed work executed their task one after another in the following way, namely, sarcasm detection, emotional-level computation, breakpoint computation, breakpoint validation, influential user generation, and information diffusion. After the successful implementation of this proposed PID-ERSDIS, it produced prominent results against other state-of-art methods.