mallet.doc.topics
(Package: mallet) :
Retrieve a matrix of topic weights for every document
This function returns a matrix with one row for every document and one column for every topic.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.doc.topics
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mallet.word.freqs
(Package: mallet) :
Descriptive statistics of word frequencies
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.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.word.freqs
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MalletLDA
(Package: mallet) :
Create a Mallet topic model trainer
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.
● Data Source:
CranContrib
● Keywords:
● Alias: MalletLDA
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mallet.topic.hclust
(Package: mallet) :
Return a hierarchical clustering of topics
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.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.topic.hclust
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This package provides an interface to the Java implementation of latent Dirichlet allocation in the Mallet machine learning package. Mallet 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.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet, mallet-package
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mallet.read.dir
(Package: mallet) :
Import documents from a directory into Mallet format
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.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.read.dir
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mallet.subset.topic.words
(Package: mallet) :
Estimate topic-word distributions from a sub-corpus
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.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.subset.topic.words
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mallet.topic.labels
(Package: mallet) :
Get strings containing the most probable words for each topic
This function returns a vector of strings, one for each topic, with the most probable words in that topic separated by spaces.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.topic.labels
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mallet.topic.words
(Package: mallet) :
Retrieve a matrix of words weights for topics
This function returns a matrix with one row for every topic and one column for every word in the vocabulary.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.topic.words
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mallet.import
(Package: mallet) :
Import text documents into Mallet format
This function takes an array of document IDs and text files (as character strings) and converts them into a Mallet instance list.
● Data Source:
CranContrib
● Keywords:
● Alias: mallet.import
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