English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments

Online teaching and learning has been beneficial in facilitating of English as a foreign language (EFL). In online EFL learning, YouTube is one the most utilized information communication technology (ICT) tools because its inherent features that make it unique environment for learners educators. Many interesting aspects YouTube-based can be supplementing conventional classroom methods, and, therefore, such must identified. Previous scholarly work aimed at improving was predominantly conducted manually by gathering learners’ impressions through interviews questionnaires to analyze differences between YouTube- classroom-based learning. However, methods are tedious time-consuming lead results less generalizable implications. User comments on channels useful identifying aspects, they present wealth related quality content provided, challenges targeted audience faces, areas potential improvement. Therefore, our current study, API used collect three randomly selected popular channels. Following data cleaning process, people’s sentiments about were first identified via TextBlob method. Second, automated latent semantic analysis (LSA) method topic finding global open-ended topics discussion Users’ discussed this paper. Further, based results, hypothetical findings provided recommendation future content, including more variety covered, introduction meanings punctuation following words, design course addresses multinational any age, each English, British American. We also suggestions who wish utilize offline which include interest one’s needs with tutor or teacher optimize experience, participating fearless educator–learner interaction engagement, asking other their previous experiences order learner maximize benefit.