INSIGHTS FROM THE TUBE: ANALYZING YOUTUBE COMMENTS WITH SUPPORT VECTOR MACHINES

Abstract

The vast ecosystem of YouTube has not only revolutionized content consumption but has also become a dynamic platform for global interactions through comments. This research paper delves into the intriguing realm of "YouTube Comment Analysis Using Support Vector Machine." The objective is to harness the power of Support Vector Machines (SVM) in deciphering the wealth of information contained within YouTube comments. In an era of abundant user-generated content, the analysis of YouTube comments has the potential to unveil valuable insights, ranging from sentiment analysis to user behavior patterns. This study outlines a methodology that leverages SVM, a robust machine learning algorithm, to classify and dissect the myriad of comments into meaningful categories. The process involves data collection, preprocessing, feature extraction, and the application of SVM for comment classification. Through this, we aim to uncover trends, sentiments, and user sentiments across various YouTube channels and content genres. Furthermore, we explore the implications of this analysis for content creators, marketers, and platform administrators in understanding and engaging with their audience more effectively. As YouTube continues to be a primary source of information and entertainment, understanding the dynamics of YouTube comments through SVM-driven analysis holds tremendous promise. This paper not only presents a technical approach but also discusses the broader impact of YouTube comment analysis in the digital age, shedding light on the evolving landscape of online interactions.