Application of Decision Tree‐Based Classification Algorithm on Content Marketing

Abstract

Traditional content marketing methods resort grossly to market requirements but barely obtain relatively accurate marketing prediction under loads of requirements. Machine learning‐based approaches nowadays are widely used in multiple fields as they involve a training process to deal with big data problems. In this paper, decision tree‐based methods are introduced to the field of content marketing, and decision tree‐based methods intrinsically follow the process of human decision making. Specifically, this paper considers a well‐known method, called C4.5, which can deal well with continuous values. Based on four validation metrics, experimental results obtained from several machine learning‐based methods indicate that the C4.5‐based decision tree method has the ability to handle the content marketing dataset. The results show that the decision tree‐based method can provide reasonable and accurate suggestions for content marketing.