Being able to quickly summarize a piece of literature is something that is quite useful before deciding whether or not you want to read it. However, we all know judging a book by its cover isn't quite the right thing to do. However, being able to quickly determine the "mood" throughout the piece would be a good place to start.

What it does

Applies a naive bayes classifier to the given document, and uses this to determine the average mood of each sentence, and from that, determines the average mood throughout the document.

How I built it

National Language ToolKit supplied the data for the classifier, and python allowed a convenient way to implement this model.

Challenges I ran into

Building the Classifier without adequate test data Getting python to play with audio files nicely

What's next for Sensing Semantics

Getting better test data

Share this project: