Design and simulation of a novel classification framework for separating sentiment from assorted game related tweets

Twitter has been exceedingly a ubiquitous phenomenon with a thriving appearance in almost every country in the world. In this modern epoch, game is one of the most popular genres to tweet. Furthermore, sentiment analysis is used to analyze the patent opinion of a person by mining text units i.e. tweets. In this paper, strategy based categories of virtual games were identified; top most countries according to users were detected; foremost usable language of the users was ascertained; and advertisement from assorted game related tweets were filtered to retrieve sentiment tweets exclusively through simulation. In light of these findings, a new gestalt of sentiment analysis of virtual game related tweets is accomplished.