Package {GMTM}

Title:	Gaussian Mixture Topic Models
Version:	0.2.0
Maintainer:	Kohei Watanabe <watanabe.kohei@gmail.com>
Description:	Gaussian mixture models (GMM) and k-means for topic analysis of dense document vectors. The underlying clustering functions rely on the Armadillo library (Sanderson & Curtin, 2017) <doi:10.1109/ICSPCS.2017.8270510>.
License:	Apache License (≥ 2.0)
Encoding:	UTF-8
Depends:	R (≥ 3.5.0)
Imports:	quanteda (≥ 4.0.0), proxyC, wordvector (≥ 0.6.4), Rcpp, RcppArmadillo
Suggests:	testthat, spelling, rmarkdown, knitr, withr
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.7.600.1.0)
Language:	en-US
Config/roxygen2/version:	8.1.0
NeedsCompilation:	yes
Packaged:	2026-09-22 00:28:46 UTC; watan
Author:	Kohei Watanabe [aut, cre, cph], Sanderson Conrad [ctb, cph] (C++ code for GMM and k-means), Curtin Ryan [ctb, cph] (C++ code for GMM and k-means)
Repository:	CRAN
Date/Publication:	2026-09-22 07:50:19 UTC

Convert a dictionary to a seed word matrix

Description

Convert a dictionary to a seed word matrix

Usage

as.seedwords(x, model, residual = 0, levels = 1)

Arguments

Details

Unseeded topics are labeled "other", but it can be changed via options("GMTM.residual.name").

Value

Returns a seed word matrix.

Examples

library(quanteda)
library(wordvector)
options(wordvector_threads = 2)

corp <- head(wordvector::data_corpus_news2014, 1000)
toks <- tokens(corp, remove_punct = TRUE,
               remove_symbols = TRUE, remove_numbers = TRUE) %>%
        tokens_remove(stopwords("en"), min_nchar = 2)
wov <- textmodel_word2vec(toks, dim = 50)

dict <- dictionary(list(eco = "econom*", sec = "securit*", spo = "sport*",
                        pol = "politi*", cri = "crime"))
seed <- as.seedwords(dict, wov, residual = 2)

Compute sum of rows by a given factor

Description

Compute sum of rows by a given factor

Usage

group_matrix(x, factor, normalize = TRUE)

Arguments

Extract the probabilities of topics

Description

Extract the probabilities of topics

Usage

## S3 method for class 'textmodel_gmm'
probability(x, group = FALSE, ...)

Arguments

Details

The original doc_id is inherited from quanteda::dfm or quanteda::tokens and saved in x$dovars$docid_ as factor.

Value

Returns the probabilities of topics as a matrix.

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

wordvector

probability()

Extract words for topics from documents

Description

Identify distinctive words for each topic by applying TF-IDF weights to the original quanteda::dfm.

Usage

terms(x, data, n = 10, ...)

Arguments

Details

To identify distinctive words for topics, original documents must be provided along with a fitted model because the information about individual words are lost in document vectors. The documents in data is grouped by topic and weighted by TF-IDF to select the most distinctive words for each topic. This technique is commonly known as c-TF-IDF.

Value

Returns a character matrix with the most distinctive words for each topic.

Topic analysis using Gaussian mixture models

Description

Perform topic analysis of document vectors using Gaussian mixture models.

Usage

textmodel_gmm(
  x,
  k = 10,
  model = NULL,
  seeds = NULL,
  omit = NULL,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

Details

Users can change the number of threads for the parallel computing via options(GMTM.threads) or OMP_THREAD_LIMIT in the environmental variable. To reproduce results, set options(GMTM.threads = 1) and call set.seed() immediately before textmodel_gmm() or textmodel_kmeans().

On MacOS, only one thread is used regardless of GMTM.threads because CRAN's toolchain for the platform does not support OpenMP.

omit is used to reduce the noise in the x by applying base::svd before clustering. If it is not NULL, singular values corresponding to omit are set to zero, removing their variance in x. See Chan et al. (2020) https://doi.org/10.1080/19312458.2020.1812555 for the methodology.

The number of iterations in k-means (iter_km) and expectation maximization (iter_em) stages can be set via ....

Value

Returns a fitted textmodel_gmm object.

Examples

library(quanteda)
library(wordvector)
options(wordvector_threads = 2)

corp <- head(wordvector::data_corpus_news2014, 1000)
toks <- tokens(corp, remove_punct = TRUE,
               remove_symbols = TRUE, remove_numbers = TRUE) %>%
        tokens_remove(stopwords("en"), min_nchar = 2)
wov <- textmodel_word2vec(toks, dim = 50)
dov <- as.textmodel_doc2vec(dfm(toks), wov)

gmm <- textmodel_gmm(dov, k = 10)
table(topics(gmm))

Topic analysis using k-means

Description

Perform topic analysis of document vectors using k-means.

Usage

textmodel_kmeans(
  x,
  k = 10,
  model = NULL,
  seeds = NULL,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

Value

Returns a fitted textmodel_kmeans object.

Examples

library(quanteda)
library(wordvector)
options(wordvector_threads = 2)

corp <- head(wordvector::data_corpus_news2014, 1000)
toks <- tokens(corp, remove_punct = TRUE,
               remove_symbols = TRUE, remove_numbers = TRUE) %>%
        tokens_remove(stopwords("en"), min_nchar = 2)
wov <- textmodel_word2vec(toks, dim = 50)
dov <- as.textmodel_doc2vec(dfm(toks), wov)

km <- textmodel_kmeans(dov, k = 10)
table(topics(km))

Extract the topics of documents

Description

Extract the topics of documents

Usage

topics(x, group = FALSE, ...)

Arguments

Details

The original doc_id is inherited from quanteda::dfm or quanteda::tokens and saved in x$dovars$docid_ as factor.

Value

Returns predicted topics as a vector.