go-topics

A very basic LDA (Latent Dirichlet Allocation) implementation. Not finished by any means, maybe useful as a starting point.

Usage

Create a processor from a set of transformations of the form func(word string) (new string, keep bool):

processor := topics.NewProcessor(
  topics.Transformations{
    topics.ToLower, 
    topics.Sanitize, 
    topics.MinLen, 
    topics.GetStopwordFilter("../stopwords/en")})

Read data and apply transformations to build a corpus:

var docs = []string{
    "I like to eat broccoli and bananas.",
    "I ate a banana and spinach smoothie for breakfast.",
    "Chinchillas and kittens are cute.",
    "My sister adopted cute kittens yesterday.",
    "Look at this cute hamster munching on a piece of chinchillas.",
}
corpus, err := processor.AddStrings(topics.NewCorpus(), docs)

Run LDA and print the results:

lda := topics.NewLDA(&topics.Configuration{Verbose: true, PrintInterval: 500, PrintNumWords: 8})
err = lda.Init(corpus, 2, 0, 0) // K (number of topics), α, β (Dirichlet distribution smoothing factors)
_, err = lda.Train(1000) // Number of iterations
lda.PrintTopWords(5)

Resulting in something like:

Topic   Tokens  Words
0       9       like(1) eat(1) broccoli(1) bananas(1) ate(1)
1       14      cute(3) kittens(2) chinchillas(2) piece(1) look(1)