Text Mining and Self Organizing Maps: Visualizing Shakespeare

After my previous exploration of Self Organizing Maps, I decided to use the tool for an application of text mining: Can we visualize how Shakespeare’s characters and plays are similar or different from each other based on an analysis of their words?

This tutorial walks through a couple examples using R and suggests some further exploration. It’s split into two sequential parts:

Self Organizing Maps and Text Mining – Visualizing Shakespeare (Part 1)

Self Organizing Maps and Text Mining – Visualizing Shakespeare (Part 2)

 

Introduction to Self Organizing Maps in R

This semester I’ve been playing around with Self Organizing Maps (SOMs) using the “kohonen” package in R. SOMs allow you to visualize very high dimensional data in a simplified two dimensional map which preserves proximity. I’ve written up an introductory tutorial on getting started making SOMs using the kohonen package:

https://clarkdatalabs.github.io/soms/SOM_NBA

This workshop plays around with NBA player stats from the 2015/2016 season. Disclaimer: I know next to nothing about basketball.

Self Organizing Map depicting NBA Player Position Predictions

If you like this post, keep an eye out for the next one. In the next month I’ll put out a tutorial on using SOMs to┬ávisualize the text-mined works of Shakespeare. Disclaimer: I know next to nothing about the works of Shakespeare.