Enhancing multi-class web video categorization model using machine and deep learning approaches

Abstract

With today's digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.