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)