2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 9 , Issue 1 , PP: 38-46, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Hamzah A. Alsayadi 1 * , El-Sayed M. El-Kenawy 2 , Abdelhameed Ibrahim 3 , Marwa M. Eid 4 , Abdelaziz A. Abdelhamid 5
Doi: https://doi.org/10.54216/FPA.090103
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.
Blog feedback prediction , Ensemble model , Machine learning , Regression model , Data fusion
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