A Simple Topic Model by Allen Beye Riddell.
From the post:
NB: This is an extended version of the appendix of my paper exploring trends in German Studies in the US between 1928 and 2006. In that paper I used a topic model (Latent Dirichlet Allocation); this tutorial is intended to help readers understand how LDA works.
Topic models typically start with two banal assumptions. The first is that in a large collection of texts there exist a number of distinct groups (or sources) of texts. In the case of academic journal articles, these groups might be associated with different journals, authors, research subfields, or publication periods (e.g. the 1950s and 1980s). The second assumption is that texts from different sources tend to use different vocabulary. If we are presented with an article selected from one of two different academic journals, one dealing with literature and another with archeology, and we are told only that the word “plot” appears frequently in the article, we would be wise to guess the article comes from the literary studies journal.1
A major obstacle to understanding the remaining details about how topic models work is that their description relies on the abstract language of probability. Existing introductions to Latent Dirichlet Allocation (LDA) tend to be pitched either at an audience already fluent in statistics or at an audience with minimal background.2 This being the case, I want to address an audience that has some background in probability and statistics, perhaps at the level of the introductory texts of Hoff (2009), Lee (2004), or Kruschke (2010).
A good walk through on using a topic model (Latent Dirichlet Allocation).
I saw this in Christophe Lalanne’s Bag of Tweets for July 2012.