YOUTUBE COMMENT ANALY FOR CONTENT CREATOR

Abstract

The exponential growth of user-generated content on video-sharing platforms has created new opportunities and challenges for content creators and researchers. Among these platforms, YouTube stands as one of the most influential media-sharing environments, where millions of users upload, watch, and interact with videos daily. One of the most significant forms of interaction on YouTube is the comment section, where viewers express opinions, ask questions, provide suggestions, or share feedback regarding the content. Although these comments contain valuable insights about audience perception and engagement, the massive volume of comments generated on popular videos makes manual analysis extremely difficult, timeconsuming, and inefficient. Traditional methods such as manual moderation, keyword filtering, and simple sentiment analysis tools fail to capture contextual meaning, user intent, sarcasm, and emotional nuances present in natural language. To address these challenges, this research proposes a YouTube Comment Intelligence Platform with Auto-Learning Natural Language Processing (NLP). The system utilizes machine learning and NLP techniques to automatically classify comments based on sentiment (positive, negative, neutral) and intent categories such as praise, question, complaint, spam, hate, and suggestion. The platform integrates a web-based interface for comment interaction, a machine learning engine for text analysis, and an explainable AI mechanism that provides transparency in predictions. Additionally, the system incorporates a feedback-driven autolearning mechanism that allows the model to improve its performance over time by incorporating corrected user feedback into the training dataset. The proposed architecture combines modern web technologies, machine learning algorithms, and visualization dashboards to deliver actionable insights to content creators. The system demonstrates that intelligent automated comment analysis can significantly reduce manual workload while enabling creators to better understand audience feedback and engagement trends.