Sentiment analysis of YouTube videos comments for children using machine learning and deep learning

Abstract

Nowadays, online connectivity is increasing with the rapid growth of the world wide web. Consequently, content shared across numerous platforms varies in appropriateness. it is necessary to ensure the suitability of the content since children are among the consumers of online content. A lot of children watch videos on YouTube these days, and such platforms can contain useful content. However, such videos can also have a negative impact on children. The suitability of these videos can be determined through sentiment analysis to refine the content for children on YouTube, by classifying the posted comments as either positive or negative. Therefore, this study utilizes natural language processing methods, machine learning classifiers (MLCs) and deep learning models (DLMs) to detect and classify negative user comments using the proposed dataset. Different MLCs such as random forest (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), decision tree (DT), K-nearest neighbour (KNN), AdaBoost, and support vector machine (SVM) have been used. Additionally, DLMs were also used such as artificial neural network (ANN), convolutional neural network (CNN) and long short-term memory (LSTM). Overall, the experimental results showed that the LR, RF, AdaBoost, ANN and LSTM classifiers outperformed all the other classifiers in terms of accuracy.