Springboard Capstone Project II: Topic Model Analysis of Berkshire Hathaway's Shareholder Letters
This project is an analysis of Berkshire Hathaway's annual letters using Natural Language Processing with Python. Approaches included three types of extractive summarization: LexRank, TextRank, and Latent Semantic Analysis, as well as topic modeling using the Mallet wrapper from Gensim and a Java version of Mallet LDA.
If you plan to run this code, make sure to have Mallet downloaded as the file was too large to fit in this repo. You can do that at http://mallet.cs.umass.edu/topics.php. Make sure to set the file locations and shortcuts for Mallet to your respective files on your computer, as otherwise, the code will not run on your computer.
Files will eventually be linked below:
Final Code Final Report Final Presentation