Created
August 24, 2011 21:45
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Unsupervised topic modeling in Ruby using LDA
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| require 'lda-ruby' | |
| corpus = Lda::Corpus.new | |
| corpus.add_document(Lda::TextDocument.new(corpus, "a lion is a wild feline animal", [])) | |
| corpus.add_document(Lda::TextDocument.new(corpus, "a dog is a friendly animal", [])) | |
| corpus.add_document(Lda::TextDocument.new(corpus, "a cat is a feline animal", [])) | |
| lda = Lda::Lda.new(corpus) | |
| lda.verbose = false | |
| lda.num_topics = (2) | |
| lda.em('random') | |
| topics = lda.top_words(3) | |
| # Results | |
| # => {0=>["animal", "friendly", "dog"], 1=>["animal", "feline", "cat"]} |
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LDA is short for Latent Dirichlet Allocation, an algorithm developed by David Blei, at Princeton. It is an unsupervised mechanism for surfacing topics and themes within a group of text.