Machine Learning based Sentiment Analysis of YouTube Video Comments

Abstract

User comments on YouTube videos have also increased exponentially as a result of the platform's rapid expansion. Although manually analyzing these comments can be time-consuming and challenging for content creators, they serve as a source of feedback and user engagement for that video. A method of machine learning called "sentiment analysis" can be used to categorize the comments' sentiment. The effectiveness of sentiment analysis in analyzing YouTube comments can be investigated in this study. It gathered a sizable set of comments from well-known YouTube videos, sentimentally annotated them, and fed it to various machine learning models for classification. Our findings show that YouTube comments can be accurately categorized as positive, negative, or neutral using sentiment analysis, providing valuable insights into how viewers feel about the videos and the subjects they cover.