gsdmm

---

gsdmm implements short text classification via Dirichlet Mixture Models proposed by Yin and Wang 2014. It provides a fast c++ implementation and R interface for the Gibbs sampler described in the paper. Specifically, gsdmm implements the Likelihood function allowing for multiple occurrences of the same word in a given text (EQ4).

Benefits:

• very space and time efficient
• unlike LDA it requires only an upper bound on the number of clusters

Development:

• I am planning to add a tuning function for the alpha and beta parameters of the gibbs sampler

Installation

You can install the development version of gsdmm from GitHub with:

# install.packages("devtools")
devtools::install_github("till-tietz/gsdmm")

Usage

Here is a minimal working example.

# we lemmatize and tokenize creating a list of character vector representing each text
text <- c(
  "Rockets are amazing.",
  "Witnessing a rocket in flight is a marvel of engineering.",
  "We should take a rocket to Mars.",
  "Rocket",
  "Have you ever seen a cat?",
  "Cats are fun.",
  "Your cat seems sweet.",
  "Cat"
) |>
  tolower() |>
  gsub(pattern = "[[:punct:] ]+", replacement = " ") |>
  textstem::lemmatize_strings() |>
  text2vec::word_tokenizer() |>
  lapply(function(i) i[!i %in% stopwords::stopwords()])

set.seed(42)

gsdmm::gsdmm(texts = text, n_iter = 100, n_clust = 5, alpha = 0.1, beta = 0.01, progress = FALSE)
#> $cluster
#> [1] 2 0 0 0 4 4 4 4
#> 
#> $distribution
#> 14 x 3 sparse Matrix of class "dgCMatrix"
#>          0 2 4
#> rocket   3 1 .
#> amaze    . 1 .
#> witness  1 . .
#> flight   1 . .
#> marvel   1 . .
#> engineer 1 . .
#> take     1 . .
#> mar      1 . .
#> ever     . . 1
#> see      . . 1
#> cat      . . 4
#> fun      . . 1
#> seem     . . 1
#> sweet    . . 1