TV show popularity analysis using social media, data mining

Abstract

Television group of onlookers rating is a vital pointer as to prevalence of projects and it is likewise a factor to impact the income of communicate stations through promotions. Albeit higher evaluations for a given program are gainful for the two supporters and promoters, little is thought about the components that make programs increasingly alluring to watchers. So as to think about the prevalence of performers, we consider the quantity of hits gotten by the tweets identified with them on Twitter. In this project we are using three different data mining techniques namely – Decision Tree, Naïve Bayes, and XGBoost. We are comparing each data model with other techniques so that we get the most accurate results. The overall objective of our work is to predict more accurately which tv show will gain more popularity in the future. Here, we have the option to develop a Graphical User Interface(GUI) that may assist any naïve user in evaluating a show and predict it’s success.