Topic models are a suite of algorithms that uncover the hidden thematic structure in document collections. These algorithms help us develop new ways to search, browse and summarize large archives of texts.
Below, you will find links to introductory materials, corpus browsers based on topic models, and open source software (from my research group) for topic modeling.
- I wrote a general introduction to topic modeling .
- John Lafferty and I wrote a more technical review paper about this field.
- Here are slides from some recent tutorials about topic modeling:
- Here is a video from a talk on dynamic and correlated topic models applied to the journal Science . (Here are the slides.)
- David Mimno maintains a bibliography of topic modeling papers and software.
- The topic models mailing list is a good forum for discussing topic modeling.
Corpus browsers based on topic models
The structure uncovered by topic models can be used to explore an otherwise unorganized collection. The following are browsers of large collections of documents, built with topic models.
- A 100-topic browser of the dynamic topic model fit to Science (1882-2001).
- A 100-topic browser of the correlated topic model fit to Science (1980-2000)
- A 50-topic browser of latent Dirichlet allocation fit to the 2006 arXiv.
- A 20-topic browser of latent Dirichlet allocation fit to The American Political Science Review
Topic modeling software
Our research group has released many open-source software packages
for topic modeling. Please post questions, comments, and suggestions
about this code to the topic models mailing list.
|lda-c||Latent Dirichlet allocation||C||D. Blei||This implements variational inference for LDA.|
|class-slda||Supervised topic models for classifiation||C++||C. Wang||Implements supervised topic models with a categorical response.|
|lda||R package for Gibbs sampling in many models||R||J. Chang||Implements many models and is fast . Supports LDA, RTMs (for networked documents), MMSB (for network data), and sLDA (with a continuous response).|
|online lda||Online inference for LDA||Python||M. Hoffman||Fits topic models to massive data. The demo downloads random Wikipedia articles and fits a topic model to them.|
|online hdp||Online inference for the HDP||Python||C. Wang||Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.|
|tmve (online)||Topic Model Visualization Engine||Python||A. Chaney||A package for creating corpus browsers. See, for example, Wikipedia .|
|ctr||Collaborative modeling for recommendation||C++||C. Wang||Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.|
|dtm||Dynamic topic models and the influence model||C++||S. Gerrish||This implements topics that change over time and a model of how individual documents predict that change.|
|hdp||Hierarchical Dirichlet processes||C++||C. Wang||Topic models where the data determine the number of topics. This implements Gibbs sampling.|
|ctm-c||Correlated topic models||C||D. Blei||This implements variational inference for the CTM.|
|diln||Discrete infinite logistic normal||C||J. Paisley||This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.|
|hlda||Hierarchical latent Dirichlet allocation||C||D. Blei||This implements a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.|
|turbotopics||Turbo topics||Python||D. Blei||Turbo topics find significant multiword phrases in topics.|