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An improved deep convolutional neural network-based YouTube video classification using textual features.

Ali Raza1, Faizan Younas2, Hafeez Ur Rehman Siddiqui3

  • 1Department of Software Engineering, The University of Lahore, Lahore, Pakistan.

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Summary
This summary is machine-generated.

This study introduces an AI approach using deep convolutional neural networks (DCNNs) to categorize YouTube videos. The DCNN model achieved 99% ROC AUC and 96% accuracy, outperforming other methods for effective video classification.

Keywords:
Convolutional neural networkText categorizationText featuresYouTube video categorization

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

Background:

  • Explosive growth in online video content, particularly on platforms like YouTube, necessitates advanced categorization methods.
  • YouTube hosts millions of channels and a vast daily influx of videos, overwhelming manual or basic sorting techniques.
  • Existing video categorization approaches struggle to efficiently manage and classify this massive, rapidly expanding dataset.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence-based approach for categorizing YouTube videos.
  • To leverage textual metadata (titles, descriptions, tags) for effective video classification.
  • To compare the performance of a deep convolutional neural network (DCNN) against other machine learning models for YouTube video categorization.

Main Methods:

  • Utilized YouTube exploratory data analysis (YEDA) to analyze video textual information.
  • Designed and implemented a deep convolutional neural network (DCNN) for video categorization.
  • Compared DCNN performance with Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), logistic regression, support vector machines, decision trees, and random forest models.
  • Trained and tested models on a large dataset comprising 9 distinct video classes.

Main Results:

  • The proposed deep convolutional neural network (DCNN) achieved a 99% receiver operating characteristics (ROC) area under the curve (AUC) score.
  • The DCNN model demonstrated a classification accuracy of 96%, surpassing the performance of other evaluated models.
  • Analysis confirmed that textual information is a significant factor for accurate video categorization.

Conclusions:

  • The developed DCNN-based approach offers a highly accurate and efficient method for categorizing YouTube videos.
  • This AI-driven categorization system can enhance user experience by improving video recommendations and sorting.
  • The study highlights the potential of deep learning techniques to manage and organize large-scale online video databases.