Prediction of Buzz in Social-Media Using Random Forest Algorithm

Good management of the social media monitoring process contributes to effective planning in social networks. Knowing what potential customers are talking about a product brand, about sharing trends, and communicating with them is crucial in terms of marketing strategies. Buzz is actually about how a product brand is positioned in the eyes of its users and customers. Beside this, Buzz prediction on social media channels such as Twitter is a challenging task that has been generated from real data by defining different features to represent the Buzz case. These predictions are helpful in analyzing important brands' Buzz posts of their potential customers' considerations in social networks. In the majority of our related researches, Support Vector Machine (SVM) combined with Radial Basis Function (RBF) approach was observed and investigated. In addition to executing the prediction in the research studies, the data set used is classified. In this study, we used another method in order to cope with these predictions, named Random Forest (RF). This method has one more advantage than the mentioned ones which is rank ordering of the related data set. The findings on the same data set and the comparison between the mentioned three methods showed that the RF gives the overall better accuracy result with the value of 99% and fastest training time. It is also inferred that the Buzz is a dynamic event in which the basis of prediction could be modelled on the content as well as the forest. It can detect the most significant attributes in order to identify the created topic is either Buzz or not. Finally, the use of much faster and more reliable algorithms for Buzz prediction from products and brands comments in social media is crucial.