MoodMatch

An example interaction on MoodMatch

Inspiration

Many times, it is very difficult to pick a song that is perfect for your current mood. No fear, however, MoodMatch is here! During this COVID-19 lockdown, it often gets hard for people to truly understand how they feel, let alone understand how to feel even better. This issue inspired me to create MoodMatch. I feel like this project truly encapsulates a topic that has not been addressed by many people/companies. It is a very small thing to not understand how you feel or what to listen to in order to feel better, but MoodMatch addresses just that (and hopefully more very soon).

What it does

MoodMatch is a messenger bot that recommends users' songs based on their current mood, and according to images/concepts associated with those moods.

How I built it

Before this project, I was very new to the ideas of creating a project. This project introduced me to end-to-end programming. I started by first mapping out what I had to do: 1) Obtain a list of songs, artists, and lyrics of the songs 2) Perform sentimental analysis on the songs to understand what kinds of feelings are exhibited by the song 3) Create a messenger bot 4) Program the messenger bot to ask the user questions 5) Process the results (sentimental analysis on the chosen choices) 6) Recommend songs with similar sentiment levels or perhaps higher sentiment levels to cheer one up I followed this process thoroughly and used the Microsoft Azure Cognitive Science API to perform sentimental analysis. I also used various APIs including Spotify, Genius Lyrics, and various others.

Challenges I ran into

As I was very new to the end-to-end programming process, I ran into many challenges including not being able to understand what was wrong. I often asked friends or even posted on StackOverflow to get some of my questions answered.