Multilingual Sentiment Analysis for YouTube Educational Videos Using NLP And Machine Learning Approaches

Sentiment analysis has emerged as a critical method for measuring the usefulness of YouTube educational videos in the ever-expanding ecosystem of online educational content. The goal of this project is to create a multilingual sentiment evaluation framework that analyzes and discerns the emotional tenor and attitudes depicted in these movies using machine learning and natural language processing (NLP) techniques. This work intends to provide educators, content providers, and learners with essential insights into the emotional impact of educational materials by constructing a sophisticated model capable of interpreting sentiment across many languages. By using this innovative strategy, we seek to raise the standard of online instruction and create an environment that promotes engaging and successful learning across language and cultural barriers.