TY  - JOUR
A1  - Raza, Ali
A1  - Younas, Faizan
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Rustam, Furqan
A1  - Gracia Villar, Mónica
A1  - Silva Alvarado, Eduardo René
A1  - Ashraf, Imran
TI  - An improved deep convolutional neural network-based YouTube video classification using textual features
N2  - Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category.
ID  - uninimx14337
UR  - http://doi.org/10.1016/j.heliyon.2024.e35812
SN  - 24058440
AV  - public
VL  - 10
JF  - Heliyon
KW  - YouTube video categorization; Convolutional neural network; Text categorization; Text features
IS  - 16
Y1  - 2024/08//
ER  -