Latent Dirichlet allocation

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data. LDA allows you to analyze of corpus, and extract the topics that combined to form its documents. For example, click here to see the topics estimated from a small corpus of Associated Press documents. LDA is fully described in Blei et al. (2003) .

This code contains:

• an implementation of variational inference for the per-document topic proportions and per-word topic assignments
• a variational EM procedure for estimating the topics and exchangeable Dirichlet hyperparameter

Downloads

Download the readme.txt .

Download the code: lda-c.tgz .

Sample data

2246 documents from the Associated Press [ download ].

Top 20 words from 100 topics estimated from the AP corpus [pdf].

Bug fixes and updates

To learn about bug-fixes, updates, and discuss LDA and related techniques, please join the topic-models mailing list, topic-models [at] lists.cs.princeton.edu.

To join, click here .

Other implementations on the web

• R package
• The Mallet Toolkit from UMass
• Gregor Heinrich's LDA-J
• Multinomial PCA