Semantic Analysis-based Improvement Suggestion System for YouTube Content Creators

Abstract

Creating content that meets the expectations of your target audience is crucial to achieving success on YouTube. To achieve this, analyzing the comments received from the audience is of utmost importance as it provides valuable insights into what the viewers want to see in the content. However, going through thousands of comments can be a daunting and misleading task. To address this challenge, this study aims to develop a system that will assist content creators in a more efficient and straightforward manner to understand the suggestions and improvements they are looking for from their audience. This will be achieved through the use of text classification, making it a simpler process for content creators to keep up with their audience's needs and improve their content accordingly.