A user will provide the link of the podcast he uploaded to youtube. Our project connects to youtube API to be able to extract both the video and the audio. Then it converts the audio into text, thanks to whisper and does sentiment analysis for each phrase that is said in the podcast. All this is formatted into a dataframe that is used for more in depth analysis. An statistical algorithm calculates the average emotional polarity of each of the clips and select the best three ones. The idea was to optimize for interaction in social media, and emotional polarity guarantees that it will be interesting. Finally, it outputs titles, description, subtitle files and already cropped videos to the user, so he can simple copy and paste it in its youtube. The idea for the future would be to automate the content uploads as well. This MVP is just a small piece of our vision, and our idea is to build an application that allows massive scale content distribution, identifying the best forms of repurpusing the content a user has an automatically uploading it for them to social media.
"Need market data. Love the use case. Great UX and journey. I like the summary pages/emails. Super good tool for video editors. Many new features possible. Go to production with Slingshot please. "