Authors :
Chandreyi Chowdhury; Baibhav Pathy
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://shorturl.at/iAOV5
DOI :
https://doi.org/10.5281/zenodo.7974261
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
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