S3 and Spotify Application are no longer active. Please only use the notebooks as templates. Experience an example playlist below.
Cohort 19 Capstone Project for the Graduate Certificate of Data Science at Georgetown University School of Continuing Studies.
Adam Goldstein (Project Coordinator) - https://www.linkedin.com/in/adammgoldstein1/
Patricia Merino - merinopc@gmail.com | www.linkedin.com/in/patriciamerino7
Navneet Sandhu - nav.sandhu03@gmail.com | www.linkedin.com/in/navneetksandhu2
Nicholas Merkling (Statistician) - merknc08@gmail.com | https://www.linkedin.com/in/nicholas-merkling/
We believe that creating playlists driven by lyrical content can give the user a glimpse into thematic sequences that exist within their “liked” tracks.
We believe sonic variation in a playlist can be achieved by ordering the tracks according to a valence-energy curve, thus delivering a captivating listening experience.
Spotify offers a plethora of playlists ranging from types of moods to genres. However, some playlists, such as “POLLEN”, declare itself as a genre-less playlist. Our playlist methodology expands on the idea of a genre-less playlist by connecting tracks through their lyrical message and themes rather than genre. Our product is called a Thematic Sequence Playlist (TSP). The TSP is created by collaborating with a Spotify user’s “liked tracks” to create a ten to twenty track playlist where tracks are linked through a structured lyrical theme.
POLLEN is “a playlist based around a radical premise: It was not organized by genre.” This playlist has 410,000 followers and is made up of 120 tracks. A testimony to Pollen’s listeners from Hope Tala, a producer of an artist with 450,000 monthly listeners, exclaims “We’ve been on other playlists that have higher follower numbers, but the engagement on Pollen is mad….This is not like a mid-day cafe background playlist; this is a, I-need-to-listen-to-this-today” (1).
https://open.spotify.com/playlist/0GSTuobEEOLb9k0bBJJH4R?si=LRjUmFSzSF2F-zucXuwRyg
Tracks are chosen by uni-gram. Improvement would be by n-grams.
Personal Music Collection Database - Train model to find features of “importance” to learn in order to generate TSPs.
Spotify API - Use liked tracks to make TSPs based on trained model from Personal Music Collection Database
Kaggle Data File - Train model on lyrics