Social Media Performance And Evaluation : An Approach To Business Analytics Concept

Abstract

The growth of the internet has resulted in the digitalization of data, which has led to the emergence of big data opportunities. Significant amounts of digital data leave traces of what customers see, read, do, and judge, as well as information about their interests and preferences, resulting in a large amount of data that may be mined for learning experiences. Data mining, statistical algorithms, and machine learning approaches are used in descriptive, predictive, and prescriptive analytics to analyze, forecast, and optimize what is the most take effect, future trends, events, and behaviors based on various data types. A decision support system is widely demanded in tackling this problem, especially in understanding the interactions based on the type, and time from the Facebook post about branding data sets. This work attempts to offer descriptive, predictive, and prescriptive analytics to determine whether a post is worth paying for and promoting. This study is sought for deeper observations of posts on Facebook that get a lot of interaction and loyal users by the best algorithm compared with naive Bayes and decision tree which is using Random Forest with 90.35% accuracy.