Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1306 2018 2018 Computer Science Department, Faculty of Sciences, Ibb University, Yemen Hamzah A. Alsayadi Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt El-Sayed M. El El-Kenawy Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, 35516, Mansoura Egypt Abdelhameed Ibrahim Faculty of Artifcial Intelligence, Delta University for Science and Technology, Mansoura, Egypt Marwa M. Eid Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt Abdelaziz A. Abdelhamid The last decade lead to an unbelievable growth of the importance of social media. Due to the huge amounts of documents appearing in social media, there is an enormous need for the automatic analysis of such documents. In this work, we proposed various regression models for the blog feedback prediction to be used in the data fusion environment. These models include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. The Blog Feedback dataset is used for training and evaluating the proposed models. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods. 2022 2022 38 46 10.54216/FPA.090103 https://www.americaspg.com/articleinfo/3/show/1306