seededlda (version 1.1.0)

textmodel_lda: Unsupervised Latent Dirichlet allocation

Description

Implements unsupervised Latent Dirichlet allocation (LDA). Users can run Seeded LDA by setting gamma > 0.

Usage

textmodel_lda(
  x,
  k = 10,
  max_iter = 2000,
  auto_iter = FALSE,
  alpha = 0.5,
  beta = 0.1,
  gamma = 0,
  model = NULL,
  batch_size = 1,
  verbose = quanteda_options("verbose")
)

Value

textmodel_seededlda() and textmodel_lda() returns a list of model parameters. theta is the distribution of topics over documents; phi is the distribution of words over topics. alpha and beta are the small constant added to the frequency of words to estimate theta and phi, respectively, in Gibbs sampling. Other elements in the list subject to change.

Arguments

x

the dfm on which the model will be fit.

k

the number of topics.

max_iter

the maximum number of iteration in Gibbs sampling.

auto_iter

if TRUE, stops Gibbs sampling on convergence before reaching max_iter. See details.

alpha

the value to smooth topic-document distribution.

beta

the value to smooth topic-word distribution.

gamma

a parameter to determine change of topics between sentences or paragraphs. When gamma > 0, Gibbs sampling of topics for the current document is affected by the previous document's topics.

model

a fitted LDA model; if provided, textmodel_lda() inherits parameters from an existing model. See details.

batch_size

split the corpus into the smaller batches (specified in proportion) for distributed computing; it is disabled when a batch include all the documents batch_size = 1.0. See details.

verbose

logical; if TRUE print diagnostic information during fitting.

Details

If auto_iter = TRUE, the iteration stops even before max_iter when delta <= 0. delta is computed to measure the changes in the number of words whose topics are updated by the Gibbs sampler in every 100 iteration as shown in the verbose message.

If batch_size < 1.0, the corpus is partitioned into sub-corpora of ndoc(x) * batch_size documents for Gibbs sampling in sub-processes with synchronization of parameters in every 10 iteration. Parallel processing is more efficient when batch_size is small (e.g. 0.01). The algorithm is the Approximate Distributed LDA proposed by Newman et al. (2009). User can changed the number of sub-processes used for the parallel computing via options(seededlda_threads).

To predict topics of new documents (i.e. out-of-sample), first, create a new LDA model from a existing LDA model passed to model in textmodel_lda(); second, apply topics() to the new model. The model argument takes objects created either by textmodel_lda() or textmodel_seededlda().

References

Newman, D., Asuncion, A., Smyth, P., & Welling, M. (2009). Distributed Algorithms for Topic Models. The Journal of Machine Learning Research, 10, 1801–1828.

See Also

LDA weightedLDA

Examples

# \donttest{
require(seededlda)
require(quanteda)

corp <- head(data_corpus_moviereviews, 500)
toks <- tokens(corp, remove_punct = TRUE, remove_symbols = TRUE, remove_number = TRUE)
dfmt <- dfm(toks) %>%
    dfm_remove(stopwords("en"), min_nchar = 2) %>%
    dfm_trim(max_docfreq = 0.1, docfreq_type = "prop")

lda <- textmodel_lda(dfmt, k = 6, max_iter = 500) # 6 topics
terms(lda)
topics(lda)
# }