wt16: Whales and Tires (8 documents each)

In clintpgeorge/ldamcmc: Markov chain Monte Carlo Algorithms for the Latent Dirichlet Allocation Model

Description

A subset of Wikipedia artricles under the Wikipedia categories Whales and Tires, formatted for running various Gibbs sampling algorithms of the latent Dirichlet allocation model. This dataset contains 16 Wikipedia articles.

Usage

data(wt16)

Format

vocab a vector of unique words in the corpus vocabulary.

docs a list of documents in the corpus. Each item (represents a document) is a matrix (2 X U) of word frequencies, where U represents the number of unique words in a document. Each column in the matrix represents a unique word in a document and contains

• vocabulary-id. the index of the word in the vocabulary (starts with 0)

• frequency. the relative frequency of the word in the document

docs.metadata a matrix of document (article) metadata, where each row represents a document with

• category. the Wikipedia category assigned to the article

• title. the title of the Wikipedia web article

doc.N a vector of word counts of documents in the corpus

num.docs the number of documents in the corpus

class.labels a vector of unique categories (classes) in the corpus

ds.name the corpus name (string)

ds a list of two equal-length vectors

• wid. vocabulary ids of the instances of words in the corpus (a vector)

• did. document indices of the instances of words in the corpus (a vector)

Details

############################################################################# Documents all datasets available in the R package: ldamcmc #############################################################################

Note

Created on November 21, 2015

Author(s)

Clint P. George

Source

Articles are downloaded from the English Wikipedia with the help of Media Wiki API.

See Also

Other datasets: autos-motorcycles, bop, canis, cats, felines, ibm-mac, med-christian-baseball, rec, sci, whales, wt16m, wt

clintpgeorge/ldamcmc documentation built on Feb. 22, 2020, 12:39 p.m.