lda: Latent Dirichlet Allocation

LDAR Documentation

Latent Dirichlet Allocation


Estimate a LDA model using for example the VEM algorithm or Gibbs Sampling.


LDA(x, k, method = "VEM", control = NULL, model = NULL, ...) 



Object of class "DocumentTermMatrix" with term-frequency weighting or an object coercible to a "simple_triplet_matrix" with integer entries.


Integer; number of topics.


The method to be used for fitting; currently method = "VEM" or method= "Gibbs" are supported.


A named list of the control parameters for estimation or an object of class "LDAcontrol".


Object of class "LDA" for initialization.


Optional arguments. For method = "Gibbs" an additional argument seedwords can be specified as a matrix or an object of class "simple_triplet_matrix"; the default is NULL.


The C code for LDA from David M. Blei and co-authors is used to estimate and fit a latent dirichlet allocation model with the VEM algorithm. For Gibbs Sampling the C++ code from Xuan-Hieu Phan and co-authors is used.

When Gibbs sampling is used for fitting the model, seed words with their additional weights for the prior parameters can be specified in order to be able to fit seeded topic models.


LDA() returns an object of class "LDA".


Bettina Gruen


Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Phan X.H., Nguyen L.M., Horguchi S. (2008). Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-scale Data Collections. In Proceedings of the 17th International World Wide Web Conference (WWW 2008), pages 91–100, Beijing, China.

Lu, B., Ott, M., Cardie, C., Tsou, B.K. (2011). Multi-aspect Sentiment Analysis with Topic Models. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, pages 81–88.

See Also



data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
lda_inf <- posterior(lda, AssociatedPress[21:30,])

topicmodels documentation built on April 1, 2023, 12:14 a.m.