Functions for Text Mining and Topic Modeling

An aid for text mining in R, with a syntax that should be familiar to experienced R users. Provides a wrapper for several topic models that take similarly-formatted input and give similarly-formatted output. Has additional functionality for analyzing and diagnostics for topic models.


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Functions for Text Mining and Topic Modeling

Copyright 2018 by Thomas W. Jones

An aid for text mining in R, with a syntax that is more familiar to experienced R users. Also, implements various functions related to topic modeling, making it a good topic modeling work bench.

textmineR was created with three principles in mind:

  1. Maximize interoperability within R's ecosystem
  2. Scaleable in terms of object storeage and computation time
  3. Syntax that is idiomatic to R

Please see the vignettes for more information on how to get started.

Note: there's a lot going on with textmineR at the moment, including adding functionality based on original research.

News

Reference manual

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install.packages("textmineR")

3.0.2 by Thomas Jones, 2 months ago


https://github.com/TommyJones/textmineR


Report a bug at https://github.com/TommyJones/textmineR/issues


Browse source code at https://github.com/cran/textmineR


Authors: Thomas Jones [aut, cre] , William Doane [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports methods, parallel, text2vec, stopwords, stringr, Rcpp, RcppProgress, RSpectra

Depends on Matrix

Suggests digest, dplyr, igraph, knitr, lda, MASS, rmarkdown, SnowballC, stringi, testthat, tibble, tidyr, tidytext, topicmodels, wordcloud

Linking to Rcpp, RcppArmadillo, RcppProgress

System requirements: GNU make, C++11


See at CRAN