The inspiration was a way to have an overhead view on what the sentiment of comments are on any YouTube video.

What it does

This application and api takes a YouTube video url, it then uses the YouTube api to pull all the comments from the video. It then uses pytorch through the library flair to run a sentiment analysis on all the comments. It then shows the results back to the user in a table.

How we built it

This application was built with Flask, the flair library that built on top of pytorch and using the YouTube api.

Challenges we ran into

A challenge that I faced was deploying the application to Heroku for testing. The application requires downloads of particular data models that when unzipped exceed the size limit for a free application on Heroku. I plan on trying to deploy this on a digital ocean droplet or other deployment site.

Accomplishments that we're proud of

I'm proud of getting the application up and running correctly.

What we learned

  • I learned how to clean text data before running a sentiment analysis on them.
  • I also learned how to use the flair library.

What's next for Youtube comment analyser

The next steps are:

  • To get the csv download of results feature complete
  • Ability to add a playlist or multiple video urls
  • Increase the number of comments that the user is able to pull from a YouTube video.
  • Deploy the application

Built With

Share this project: