This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatically cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVMlight). Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
require 'lda-ruby'
corpus = Lda::DataCorpus.new("data/data_file.dat")
lda = Lda::Lda.new(corpus) # create an Lda object for training
lda.em("random") # run EM algorithm using random starting points
lda.load_vocabulary("data/vocab.txt")
lda.print_topics(20) # print all topics with up to 20 words per topic
If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you encounter bugs specific to lda-ruby, please post an issue on the Github project.
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022 [pdf].
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