Jeffrey Yan

See analysis.pdf for a summary.

This program takes a RateMD corpus and generates topics using an LDA model. Also, it separates the corpus into gender-separated sub-collections and uses the ccLDA model to generate topics.

#Run programs: part1.ipynb for Part 1 task2a.ipynb for Part 2a task2b.ipynb for Part 2b EC.ipynb for Extra Credit

input_docs.txt for input (10 topics) to ccLDA output_topwords_cclda.txt for readable output from ccLDA

input_docs_k20.txt for input (20 topics) to ccLDA output_topwords_k20_cclda.txt for readable output from ccLDA