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
You can install the development version of gsdmm from GitHub with:
# install.packages("devtools")
devtools::install_github("till-tietz/gsdmm")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()])
gsdmm::gsdmm(texts = text, n_iter = 100, n_clust = 20, alpha = 0.1, beta = 0.2)
#> [1] 4 4 6 6 18 18 2 18