The rapid growth of online video-sharing platforms has significantly changed the way people create, share, and consume information. Among these platforms, YouTube has become one of the world's largest and most influential sources of digital content, with millions of videos uploaded and viewed daily. The vast amount of data generated through views, likes, comments, shares, and subscriptions provides valuable insights into audience behavior and content performance. However, manually analyzing this information is time-consuming and often fails to reveal meaningful patterns. Therefore, there is a need for an intelligent system that can automatically collect, analyze, and visualize YouTube data to support effective decision-making.

This research presents a YouTube Content Analyzer, a web-based application designed to analyze YouTube videos and channel performance using data analytics and Natural Language Processing (NLP) techniques. The system utilizes the YouTube Data API to retrieve video metadata, channel statistics, user comments, and engagement metrics in real time. The collected data is preprocessed to remove irrelevant information and improve data quality before analysis. The system then performs sentiment analysis on viewer comments to classify audience opinions as positive, negative, or neutral. This helps content creators and organizations better understand viewer reactions and overall audience satisfaction.