Inspiration

Recommends music to Twitter users based on the sentiment behind their tweet.

What it does

Analyzes the user's sentiment and mood through their text and recommends music based on that mood.

How I built it

The sentiment analyzer was built from scratch using scikit-learn, pandas, numpy and python. The training samples were downloaded from a large csv file containing tweets and pre-analyzed sentiments. This acts as a form of supervised learning for the model

The application is made with node.js with a mongoDB database hosted at mLab. The application can authenticate Twitter users using Passport.js.

Challenges I ran into

Learning machine learning from scratch. Trying to create an application by combining imcompatible languages and tools.

Accomplishments that I'm proud of

Being able to finish.

What I learned

Machine learning through scikit-learn, pandas, and numpy.

What's next for Moosik

Unique recommendations for users based on preferences and improved machine learning algorithm.

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