Next Article in Journal
End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning
Next Article in Special Issue
PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval
Previous Article in Journal
A Survey on Techniques in the Circular Formation of Multi-Agent Systems
Previous Article in Special Issue
A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images
Article

Optimizing Prediction of YouTube Video Popularity Using XGBoost

1
Department of Computer Science, SZABIST Islamabad, Islamabad 44001, Pakistan
2
School of Computing, National University of Computer and Emerging Sciences (FAST-NUCES), Karachi 75030, Pakistan
3
Department of Computer and information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
4
School of Psychology and Computer Science, University of Central Lancashire, Preston PR1 2HE, UK
5
Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
6
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
*
Authors to whom correspondence should be addressed.
Academic Editor: Amir Mosavi
Electronics 2021, 10(23), 2962; https://doi.org/10.3390/electronics10232962
Received: 27 October 2021 / Revised: 24 November 2021 / Accepted: 25 November 2021 / Published: 28 November 2021
YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos. View Full-Text
Keywords: YouTube videos; feature fusion; video popularity prediction; social networks YouTube videos; feature fusion; video popularity prediction; social networks
Show Figures

Figure 1

MDPI and ACS Style

Nisa, M.U.; Mahmood, D.; Ahmed, G.; Khan, S.; Mohammed, M.A.; Damaševičius, R. Optimizing Prediction of YouTube Video Popularity Using XGBoost. Electronics 2021, 10, 2962. https://doi.org/10.3390/electronics10232962

AMA Style

Nisa MU, Mahmood D, Ahmed G, Khan S, Mohammed MA, Damaševičius R. Optimizing Prediction of YouTube Video Popularity Using XGBoost. Electronics. 2021; 10(23):2962. https://doi.org/10.3390/electronics10232962

Chicago/Turabian Style

Nisa, Meher U., Danish Mahmood, Ghufran Ahmed, Suleman Khan, Mazin A. Mohammed, and Robertas Damaševičius. 2021. "Optimizing Prediction of YouTube Video Popularity Using XGBoost" Electronics 10, no. 23: 2962. https://doi.org/10.3390/electronics10232962

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop