Publication Date: 2023/05/24
Abstract: 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.
Keywords: YouTube, Sentiment Analysis, Classification, Decision Insights, Case Study
DOI: https://doi.org/10.5281/zenodo.7974261
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY963.pdf
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