History of the United States through the lens of Harvard Business Review Articles (1922 - 2012)
A text mining project for HBR articles in 90 years.
In this project, I use a multivariate technique called Correspondence Analysis (CA). Given a term-year matrix that describe how many times a term j have been mentioned in year (or group of years) j, CA produces a set of orthognal components (just like Principal Component Analysis PCA) that capture the "driving forces" of variance in a dataset.
How to read the plot ?
The plot shows a representative subset of words across all years. You can imagine a spring between each word and all the years. The strenth of the spring is weighted by the number of times a word has been mentioned in that year. This way, words associated with 30's will pull those years while words associated with recent years will pull in a different direction. The plot approximates this sort of image.
More technical describtions can be found in Wikipedia: