期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：17
页码：4097
出版社：Journal of Theoretical and Applied
摘要：Social media networks such as Twitter and Facebook plays important roles in many aspects of our lives and affects many of our decisions. This paper presents a data mining model consists of different five classification and regression algorithms to predict the significant performance metrics of posts announced in the Facebook pages of the brands. The algorithms utilized in the model include the Generalized Liner Regression (GLR), Normal Regression (NR), support Vector Machine, Neural Network, and CHAID decision tree classifier. Using a dataset contained a 790 published posts in the cosmetic brand, the Lifetime post consumers achieved the best posts performance metrics with an average accuracy of 0.82 among all the algorithms in the proposed model, followed by the Lifetime post total reach performance metric with an average accuracy of 0.79. The findings of this research potentially help the manager's in making the right decisions regarding whether to publish a post.
关键词：Data Mining; Classification; Social Media; Brand Building; Performance Metrics
