This project implements Gibbs sampling inference to LDA(Latent Dirichlet Allocation).

To-do:

• Chenk convergence
• speed up Gibbs sampling process

Reference:

@article{heinrich2005parameter, title={Parameter estimation for text analysis}, author={Heinrich, G.}, journal={Web: http://www.arbylon.net/publications/text-est.pdf}, year={2005} }

Note:

• The Gibbs sampling is very slow and it is hard to check convergence.
• The result is not very good; maybe because the corpus is not very large.
• The result can be very different in different runs.

Topic modeling tools:

• David Blei's collection: http://www.cs.princeton.edu/~blei/topicmodeling.html
• Mallet from UMass: http://mallet.cs.umass.edu/
• Stanford Topic Modeling Toolbox: http://nlp.stanford.edu/software/tmt/tmt-0.4/
• Matlab Topic Modeling Toolbox by Mark Steyvers and Tom Griffiths: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
• LDA-J : http://www.arbylon.net/projects/
• R package: topicmodels and Topic models in R
• topic-modeling-tool(A grapical user interface tool based on Mallet): http://code.google.com/p/topic-modeling-tool/
• Gensim: Topic modeling for humans
• GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation