Package 'mallet' reference manual

Title:	An R Wrapper for the Java Mallet Topic Modeling Toolkit
Description:	An R interface for the Java Machine Learning for Language Toolkit (mallet) <https://mimno.github.io/Mallet/> to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into 'mallet' from R objects, run the Java implementation of 'mallet' directly in R, and extract results as R objects. The 'mallet' toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the 'rJava' package to connect to a Java Virtual Machine (JVM).
Authors:	Måns Magnusson [cre, aut] , David Mimno [aut, cph]
Maintainer:	Måns Magnusson <[email protected]>
License:	MIT + file LICENSE
Version:	1.3.1
Built:	2024-10-27 03:33:52 UTC
Source:	https://github.com/mimno/rmallet

An R Wrapper for the Java Mallet Topic Modeling Toolkit

Description

An R interface for the Java Machine Learning for Language Toolkit (mallet) <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM.

References

The model, Latent Dirichlet allocation (LDA): David M Blei, Andrew Ng, Michael Jordan. Latent Dirichlet Allocation. J. of Machine Learning Research, 2003.

The Java toolkit: Andrew Kachites McCallum. The Mallet Toolkit. 2002.

Details of the fast sparse Gibbs sampling algorithm: Limin Yao, David Mimno, Andrew McCallum. Streaming Inference for Latent Dirichlet Allocation. KDD, 2009.

Hyperparameter optimization: Hanna Wallach, David Mimno, Andrew McCallum. Rethinking LDA: Why Priors Matter. NIPS, 2010.

Load a Mallet state into Mallet

Description

This reads writes a current sampling state of mallet to file. The state contain hyperparameters $\alpha$ and $\beta$ together with topic indicators.

Usage

load.mallet.state(topic.model, state.file)
load.mallet.state(topic.model, state.file)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`state.file`	File path to store the mallet state file to.

Value

a java cc.mallet.topics.RTopicModel object

Return the mallet jar filename(s)

Description

Return the mallet jar filename(s)

Usage

mallet_jar(full.names = FALSE)

mallet.jar(full.names = FALSE)
mallet_jar(full.names = FALSE)

mallet.jar(full.names = FALSE)

Arguments

full.names

a logical value. If TRUE, the directory path is prepended to the file names to give a relative file path. If FALSE, the file name(s) (rather than paths) are returned.

Details

Mallet is implemented as a jar-file in the mallet R package. This function returns the file name and file path for that file(s)

Return the file path to the mallet stoplists

Description

Return the file path to the mallet stoplists

Usage

mallet_stoplist_file_path(language = "en")

mallet.stoplist.file.path(language = "en")
mallet_stoplist_file_path(language = "en")

mallet.stoplist.file.path(language = "en")

Arguments

language

language to return stoplist for. Defaults to engligs ([en]).

Details

Returns the path to the mallet stop word list. See [mallet_supported_stoplists()] for which stoplists that are included.

Mallet supported stoplists

Description

Mallet supported stoplists

Usage

mallet_supported_stoplists()

mallet.supported.stoplists()
mallet_supported_stoplists()

mallet.supported.stoplists()

Details

return vector with included stoplists

Retrieve a matrix of topic weights for every document

Description

This function returns a matrix with one row for every document and one column for every topic.

Usage

mallet.doc.topics(topic.model, normalized = FALSE, smoothed = FALSE)
mallet.doc.topics(topic.model, normalized = FALSE, smoothed = FALSE)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`normalized`	If `TRUE`, normalize the rows so that each document sums to one. If `FALSE`, values will be integers (possibly plus the smoothing constant) representing the actual number of words of each topic in the documents.
`smoothed`	If `TRUE`, add the smoothing parameter for the model (initial value specified as `alpha.sum` in `MalletLDA`). If `FALSE`, many values will be zero.

Value

a number of documents by number of topics matrix.

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)


## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

Import text documents into Mallet format

Description

This function takes an array of document IDs and text files (as character strings) and converts them into a Mallet instance list.

Usage

mallet.import(
  id.array = NULL,
  text.array,
  stoplist = "",
  preserve.case = FALSE,
  token.regexp = "[\\p{L}]+"
)
mallet.import(
  id.array = NULL,
  text.array,
  stoplist = "",
  preserve.case = FALSE,
  token.regexp = "[\\p{L}]+"
)

Arguments

`id.array`	An array of document IDs. Default is `text.array` index.
`text.array`	A character vector with each element containing a document.
`stoplist`	The name of a file containing stopwords (words to ignore), one per line, or a character vector containing stop words. If the file is not in the current working directory, you may need to include a full path. Default is no stoplist.
`preserve.case`	By default, the input text is converted to all lowercase.
`token.regexp`	A quoted string representing a regular expression that defines a token. The default is one or more unicode letter: "[\\p{L}]+". Note that special characters must have double backslashes.

Value

a cc/mallet/types/InstanceList object.

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")


## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")


## End(Not run)

Import documents from a directory into Mallet format

Description

This function takes a directory path as its only argument and returns a data.frame with two columns: <id> & <text>, which can be passed to the mallet.import function. This data.frame has as many rows as there are files in the Dir.

Usage

mallet.read.dir(Dir)
mallet.read.dir(Dir)

Arguments

Dir

The path to a directory containing one document per file.

Value

a data.frame with file id and text content.

Note

This function was contributed to RMallet by Dan Bowen.

Author(s)

Dan Bowen

Examples

## Not run: 
directory <- system.file("stoplists", package = "mallet")
stoplists <- mallet.read.dir(directory)

## End(Not run)

## Not run: 
directory <- system.file("stoplists", package = "mallet")
stoplists <- mallet.read.dir(directory)

## End(Not run)

Estimate topic-word distributions from a sub-corpus

Description

This function returns a matrix of word probabilities for each topic similar to mallet.topic.words, but estimated from a subset of the documents in the corpus. The model assumes that topics are the same no matter where they are used, but we know this is often not the case. This function lets us test whether some words are used more or less than we expect in a particular set of documents.

Usage

mallet.subset.topic.words(
  topic.model,
  subset.docs,
  normalized = FALSE,
  smoothed = FALSE
)
mallet.subset.topic.words(
  topic.model,
  subset.docs,
  normalized = FALSE,
  smoothed = FALSE
)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`subset.docs`	A logical vector of `TRUE`/`FALSE` values specifying which documents should be used/included and which should be ignored.
`normalized`	If `TRUE`, normalize the rows so that each topic sums to one. If `FALSE`, values will be integers (possibly plus the smoothing constant) representing the actual number of words of each type in the topics.
`smoothed`	If `TRUE`, add the smoothing parameter for the model (initial value specified as `beta` in `MalletLDA`). If `FALSE`, many values will be zero.

Value

a number of topics by vocabulary size matrix for the the included documents.

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract subcorpus topic word matrix
post1975_topic_words <- mallet.subset.topic.words(topic.model, sotu[["year"]] > 1975)
mallet.top.words(topic.model, word.weights = post1975_topic_words[2,], num.top.words = 5)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract subcorpus topic word matrix
post1975_topic_words <- mallet.subset.topic.words(topic.model, sotu[["year"]] > 1975)
mallet.top.words(topic.model, word.weights = post1975_topic_words[2,], num.top.words = 5)

## End(Not run)

Get the most probable words and their probabilities for one topic

Description

This function returns a data frame with two columns, one containing the most probable words as character values, the second containing the weight assigned to that word in the word weights vector you supplied.

Usage

mallet.top.words(topic.model, word.weights, num.top.words = 10)
mallet.top.words(topic.model, word.weights, num.top.words = 10)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`word.weights`	A vector of word weights for one topic, usually a row from the `topic.words` matrix from `mallet.topic.words`.
`num.top.words`	The number of most probable words to return. If not specified, defaults to 10.

Value

a data.frame with the top terms (term) and their weights/probability (weight).

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract top words
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract top words
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

Return a hierarchical clustering of topics

Description

Returns a hierarchical clustering of topics that can be plotted as a dendrogram. There are two ways of measuring topic similarity: topics may contain the some of the same words, or the may appear in some of the same documents. The balance parameter allows you to interpolate between the similarities determined by these two methods.

Usage

mallet.topic.hclust(
  doc.topics,
  topic.words,
  balance = 0.3,
  method = "euclidean",
  ...
)
mallet.topic.hclust(
  doc.topics,
  topic.words,
  balance = 0.3,
  method = "euclidean",
  ...
)

Arguments

`doc.topics`	A documents by topics matrix of topic probabilities (see `mallet.doc.topics`).
`topic.words`	A topics by words matrix of word probabilities (see `mallet.topic.words`) .
`balance`	A value between 0.0 (use only document-level similarity) and 1.0 (use only word-level similarity).
`method`	method to use in `dist` to compute distance between topics. Defaults to `euclidian`.
`...`	Further arguments for `hclust`.

Value

An object of class hclust which describes the tree produced by the clustering process.

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Create hiearchical clusters of topics
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
topic_labels <- mallet.topic.labels(topic.model)
plot(mallet.topic.hclust(doc_topics, topic_words, balance = 0.3), labels=topic_labels)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Create hiearchical clusters of topics
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
topic_labels <- mallet.topic.labels(topic.model)
plot(mallet.topic.hclust(doc_topics, topic_words, balance = 0.3), labels=topic_labels)

## End(Not run)

Get strings containing the most probable words for each topic

Description

This function returns a vector of strings, one for each topic, with the most probable words in that topic separated by spaces.

Usage

mallet.topic.labels(topic.model, topic.words = NULL, num.top.words = 3, ...)
mallet.topic.labels(topic.model, topic.words = NULL, num.top.words = 3, ...)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`topic.words`	The matrix of topic-word weights returned by `mallet.topic.words` Default (NULL) is to use the `topic.model` to extract the `topic.words`.
`num.top.words`	The number of words to include for each topic. Defaults to 3.
`...`	Further arguments supplied to `mallet.topic.words`.

Value

a character vector with one element per topic

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Create hiearchical clusters of topics
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
topic_labels <- mallet.topic.labels(topic.model)
plot(mallet.topic.hclust(doc_topics, topic_words, balance = 0.3), labels=topic_labels)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Create hiearchical clusters of topics
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
topic_labels <- mallet.topic.labels(topic.model)
plot(mallet.topic.hclust(doc_topics, topic_words, balance = 0.3), labels=topic_labels)

## End(Not run)

Load (read) and save (write) a topic from a file

Description

This function returns the topic model loaded from a file or stores a topic model to file.

Usage

mallet.topic.model.read(filename)

mallet.topic.model.load(filename)

mallet.topic.model.write(topic.model, filename)

mallet.topic.model.save(topic.model, filename)
mallet.topic.model.read(filename)

mallet.topic.model.load(filename)

mallet.topic.model.write(topic.model, filename)

mallet.topic.model.save(topic.model, filename)

Arguments

`filename`	The mallet topic model file
`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.

Retrieve a matrix of words weights for topics

Description

This function returns a matrix with one row for every topic and one column for every word in the vocabulary.

Usage

mallet.topic.words(topic.model, normalized = FALSE, smoothed = FALSE)
mallet.topic.words(topic.model, normalized = FALSE, smoothed = FALSE)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`normalized`	If `TRUE`, normalize the rows so that each topic sums to one. If `FALSE`, values will be integers (possibly plus the smoothing constant) representing the actual number of words of each type in the topics.
`smoothed`	If `TRUE`, add the smoothing parameter for the model (initial value specified as `beta` in `MalletLDA`). If `FALSE`, many values will be zero.

Value

a number of topics by vocabulary size matrix.

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

Descriptive statistics of word frequencies

Description

This method returns a data frame with one row for each unique vocabulary word, and three columns: the word as a character value, the total number of tokens of that word type, and the total number of documents that contain that word at least once. This information can be useful in identifying candidate stopwords.

Usage

mallet.word.freqs(topic.model)
mallet.word.freqs(topic.model)

Arguments

topic.model

A cc.mallet.topics.RTopicModel object created by MalletLDA.

Value

a data.frame with the word type (word), the word frequency (word.freq), and the document frequency (doc.freq)

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Get word frequencies
word_freqs <- mallet.word.freqs(topic.model)


## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Get word frequencies
word_freqs <- mallet.word.freqs(topic.model)


## End(Not run)

Create a Mallet topic model trainer

Description

This function creates a java cc.mallet.topics.RTopicModel object that wraps a Mallet topic model trainer java object, cc.mallet.topics.ParallelTopicModel. Note that you can call any of the methods of this java object as properties. In the example below, I make a call directly to the topic.model$setAlphaOptimization(20, 50) java method, which passes this update to the model itself.

Usage

MalletLDA(num.topics = 10, alpha.sum = 5, beta = 0.01)
MalletLDA(num.topics = 10, alpha.sum = 5, beta = 0.01)

Arguments

`num.topics`	The number of topics to use. If not specified, this defaults to 10.
`alpha.sum`	This is the magnitude of the Dirichlet prior over the topic distribution of a document. The default value is 5.0. With 10 topics, this setting leads to a Dirichlet with parameter $\alpha_k = 0.5$ . You can intuitively think of this parameter as a number of "pseudo-words", divided evenly between all topics, that are present in every document no matter how the other words are allocated to topics. This is an initial value, which may be changed during training if hyperparameter optimization is active.
`beta`	This is the per-word weight of the Dirichlet prior over topic-word distributions. The magnitude of the distribution (the sum over all words of this parameter) is determined by the number of words in the vocabulary. Again, this value may change due to hyperparameter optimization.

Value

a cc.mallet.topics.RTopicModel object

Examples

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

## Not run: 
# Read in sotu example data
data(sotu)
sotu.instances <-
   mallet.import(id.array = row.names(sotu),
                 text.array = sotu[["text"]],
                 stoplist = mallet_stoplist_file_path("en"),
                 token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")

# Create topic model
topic.model <- MalletLDA(num.topics=10, alpha.sum = 1, beta = 0.1)
topic.model$loadDocuments(sotu.instances)

# Train topic model
topic.model$train(200)

# Extract results
doc_topics <- mallet.doc.topics(topic.model, smoothed=TRUE, normalized=TRUE)
topic_words <- mallet.topic.words(topic.model, smoothed=TRUE, normalized=TRUE)
top_words <- mallet.top.words(topic.model, word.weights = topic_words[2,], num.top.words = 5)

## End(Not run)

Load and save mallet instances from/to file

Description

This function returns the topic model loaded from a file.

Usage

save.mallet.instances(instances, filename)

load.mallet.instances(filename)
save.mallet.instances(instances, filename)

load.mallet.instances(filename)

Arguments

`instances`	An `cc/mallet/types/InstanceList` `instanceList` object to save/write to file.
`filename`	The filename to save to or load from.

Save a Mallet state to file

Description

This function writes a current sampling state of mallet to file. The state contain hyperparameters $\alpha$ and $\beta$ together with topic indicators.

The state file can be read into R using the function

Usage

save.mallet.state(topic.model, state.file)
save.mallet.state(topic.model, state.file)

Arguments

`topic.model`	A `cc.mallet.topics.RTopicModel` object created by `MalletLDA`.
`state.file`	File path (.gz format) to store the mallet state file to.

State of the Union Adresses.

Description

A dataset containing State of the Union Adresses by paragraph from 1946 to 2000.

Usage

sotu
sotu

Format

A tibble data.frame with 6816 rows and 3 variables:

year: Year of the adress.
paragraph: The paragraph of the address.
text: The address content.

Source

https://en.wikipedia.org/wiki/State_of_the_Union

Package 'mallet'

Help Index

An R Wrapper for the Java Mallet Topic Modeling Toolkit

Description

References

Load a Mallet state into Mallet

Description

Usage

Arguments

Value

Return the mallet jar filename(s)

Description

Usage

Arguments

Details

Return the file path to the mallet stoplists

Description

Usage

Arguments

Details

Mallet supported stoplists

Description

Usage

Details

Retrieve a matrix of topic weights for every document

Description

Usage

Arguments

Value

Examples

Import text documents into Mallet format

Description

Usage

Arguments

Value

See Also

Examples

Import documents from a directory into Mallet format

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Estimate topic-word distributions from a sub-corpus

Description

Usage

Arguments

Value

See Also

Examples

Get the most probable words and their probabilities for one topic

Description

Usage

Arguments

Value

Examples

Return a hierarchical clustering of topics

Description

Usage

Arguments

Value

See Also

Examples

Get strings containing the most probable words for each topic

Description

Usage

Arguments

Value

See Also

Examples

Load (read) and save (write) a topic from a file

Description

Usage

Arguments

Retrieve a matrix of words weights for topics

Description

Usage