DJ Mood

Charalampos Kokkalis, Giannis Tyrovolas, Ioannis Stamoulis


​Our hack is an ambitious way of connecting moods with songs. As people who enjoy music we found it very common that algorithms entrench our pre-existing preferences when it comes to music. We wanted to create a new way to explore new music. That's why we created DJ Mood. We use natural language processing to identify the user's emotions and create a personalized playlist for each user.

Creating this hack was great fun and we learned a lot of new things in the process.


The high-level plan was the following:

0a. Analyze features of a large dataset of songs 0b. Create a website and a simple interface for the user

  1. Use input data to create a mood vector
  2. Associate our mood vector with each song by training appropriate weights
  3. Create a link with the user's new playlist

Analyse features of songs

We chose as our dataset Spotify's top 100 playlists. We did this because it includes a diverse set of musical genres and moods. With playlists from RapCaviar to Mellow Piano we believe a huge range was covered.

We used Python's Spotipy API to gather this data. ​

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