A Sentiment Analysis Case Study to Understand How a Youtuber can Derive Decision Insights from Comments

YouTube is considered the biggest platform for content creators to share their content with the world. Usually, a YouTuber aims to give his/her viewers the best content possible by going through the comments of their past videos. On average, the comments can go up to 10 thousand; hence, it becomes practically impossible to go through every comment and get an idea of what the viewers want or expect. Our work provides a model based on Python that extracts the comments of a YouTube video which then becomes our dataset. A Machine Learning techniqueknown as Sentiment Analysis (Classification Model) is applied to the dataset extracted to provide the YouTuber with a better understanding of the distribution of the sentiment of his/ her viewers, which in turn helps them get an idea of the thoughts of the viewers and also what the viewers expect from their future videos.