tidytext (version 0.2.6)

mallet_tidiers: Tidiers for Latent Dirichlet Allocation models from the mallet package

Description

Tidy LDA models fit by the mallet package, which wraps the Mallet topic modeling package in Java. The arguments and return values are similar to lda_tidiers.

Usage

# S3 method for jobjRef
tidy(
  x,
  matrix = c("beta", "gamma"),
  log = FALSE,
  normalized = TRUE,
  smoothed = TRUE,
  ...
)
# S3 method for jobjRef
augment(x, data, ...)

Arguments

x

A jobjRef object, of type RTopicModel, such as created by MalletLDA.

matrix

Whether to tidy the beta (per-term-per-topic, default) or gamma (per-document-per-topic) matrix.

log

Whether beta/gamma should be on a log scale, default FALSE

normalized

If true (default), normalize so that each document or word sums to one across the topics. If false, values will be integers representing the actual number of word-topic or document-topic assignments.

smoothed

If true (default), add the smoothing parameter to each to avoid any values being zero. This smoothing parameter is initialized as alpha.sum in MalletLDA.

...

Extra arguments, not used

data

For augment, the data given to the LDA function, either as a DocumentTermMatrix or as a tidied table with "document" and "term" columns.

Value

augment must be provided a data argument containing one row per original document-term pair, such as is returned by tdm_tidiers, containing columns document and term. It returns that same data with an additional column .topic with the topic assignment for that document-term combination.

Details

Note that the LDA models from MalletLDA are technically a special case of S4 objects with class jobjRef. These are thus implemented as jobjRef tidiers, with a check for whether the toString output is as expected.

See Also

lda_tidiers, mallet.doc.topics, mallet.topic.words

Examples

# NOT RUN {
# }
# NOT RUN {
library(mallet)
library(dplyr)

data("AssociatedPress", package = "topicmodels")
td <- tidy(AssociatedPress)

# mallet needs a file with stop words
tmp <- tempfile()
writeLines(stop_words$word, tmp)

# two vectors: one with document IDs, one with text
docs <- td %>%
  group_by(document = as.character(document)) %>%
  summarize(text = paste(rep(term, count), collapse = " "))

docs <- mallet.import(docs$document, docs$text, tmp)

# create and run a topic model
topic_model <- MalletLDA(num.topics = 4)
topic_model$loadDocuments(docs)
topic_model$train(20)

# tidy the word-topic combinations
td_beta <- tidy(topic_model)
td_beta

# Examine the four topics
td_beta %>%
  group_by(topic) %>%
  top_n(8, beta) %>%
  ungroup() %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta)) +
  geom_col() +
  facet_wrap(~ topic, scales = "free") +
  coord_flip()

# find the assignments of each word in each document
assignments <- augment(topic_model, td)
assignments
# }
# NOT RUN {
# }