Estimate a LDA model using for example the VEM algorithm or Gibbs Sampling.
1 
x 
Object of class 
k 
Integer; number of topics. 
method 
The method to be used for fitting; currently

control 
A named list of the control parameters for estimation
or an object of class 
model 
Object of class 
... 
Optional arguments. For 
The C code for LDA from David M. Blei and coauthors is used to estimate and fit a latent dirichlet allocation model with the VEM algorithm. For Gibbs Sampling the C++ code from XuanHieu Phan and coauthors 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 Largescale 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). Multiaspect Sentiment Analysis with Topic Models. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, pages 81–88.
1 2 3  data("AssociatedPress", package = "topicmodels")
lda < LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
lda_inf < posterior(lda, AssociatedPress[21:30,])

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