textmineR 3.0.5

This version is a patch. In this version I have

• Fixed a bug in CalcHellignerDist() and CalcJSDivergence() that sometimes caused inputs to be overwritten.
• Fixed some typos in the vignette for topic modeling
• Updated the documentation on FitCtmModel() to better explain how to pass control arguments to CTM’s underlying function.
• Enabled return of a tibble or data.frame (instead of only data.frame) in the following functions: SummarizeTopics, GetTopTerms, TermDocFreq (Thanks to Mattias for the PR)

textmineR 3.0.4

This version is a patch. In this version I have

• Removed unconditional stripping in MAKEVARs as specified by CRAN
• Improved outputs of FitLdaModel

textmineR 3.0.3

This version is a patch. In this version I have

• fixed an error related to the update.lda_topic_model method.
• added a method posterior.lda_topic_model to sample from the posterior of an LDA topic model.

textmineR 3.0.2

This version is a patch. In this version I have

• changed some elements of NAMESPACE to pass additional CRAN checks.
• added an update method for the lda_topic_model class. This allows users to add documents to an existing model (and even add new topics) without changing the indices of previously-trained topics. e.g. topic 5 is still topic 5.
• added a vignette for using tidytext alongside textmineR

textmineR 3.0.1

This version is a patch in response to issues revealed by automatic checks upon submission to CRAN plus an additional issue I encountered along the way.

I have * Used the CRAN template for my MIT LICENSE file * Modified the example of the LabelTopics function to speed up run time for that example * Modified vignettes to run in less time * Added a Makevars file to keep compiled code small on Ubuntu.

Please read below for major updates between v2.x.x and v3.x.x

textmineR 3.0.0

This version significantly changes textmineR.

• Several functions that were slated for deletion in version 2.1.3 are now gone.

  • RecursiveRbind
  • Vec2Dtm
  • JSD
  • HellDist
  • GetPhiPrime
  • FormatRawLdaOutput
  • Files2Vec
  • DepluralizeDtm
  • CorrectS
  • CalcPhiPrime

• FitLdaModel has changed significantly.

  • Now only Gibbs sampling is a supported training method. The Gibbs sampler is no longer wrapping lda::lda_collapsed_gibbs_sampler. It is now native to textmineR. It’s a little slower, but has additional features.
  • Asymmetric priors are supported for both alpha and beta.
  • There is an option, optimize_alpha, which updates alpha every 10 iterations based on the value of theta at the current iteration.
  • The log likelihood of the data given estimates of phi and theta is optionally calculated every 10 iterations.
  • Probabilistic coherence is optionally calculated at the time of model fit.
  • R-squared is optionally calculated at the time of model fit.

• Supported topic models (LDA, LSA, CTM) are now object-oriented, creating their own S3 classes. These classes have their own predict methods, meaning you do not have to do your own math to make predictions for new documents.

• A new function SummarizeTopics has been added.

• tm is no longer a dependency for stopwords. We now use the stopwords package. The extended result of this is that there is no longer any Java dependency.

• Several packages have been moved from “Imports” to “Suggests”. The result is a faster install and lower likelihood of install failure based on packages with system dependencies. (Looking at you, topicmodels!)

• Finally, I have changed the textmineR license to the MIT license. Note, however, that some dependencies may have more restrictive licenses. So if you’re looking to use textmineR in a commercial project, you may want to dig deeper into what is/isn’t permissable.

textmineR 2.1.3

• Deprecating functions that will be removed, renamed, or have significant changes to syntax or functionality in the forthcoming textmineR v3.0.
• Functions slated for deletion:
  • RecursiveRbind
  • Vec2Dtm
  • JSD
  • HellDist
  • GetPhiPrime
  • FormatRawLdaOutput
  • Files2Vec
  • DepluralizeDtm
  • CorrectS
  • CalcPhiPrime
• In addition: FitLdaModel is going to change significantly in its functionality and argument calls.

textmineR 2.1.2

• Deprecated RecursiveRbind - it depended on a deprecated function from the Matrix package. And the replacement offered by Matrix operates recursively, making this function truly superfluous.

textmineR 2.1.1

• Corrected some code in the vignettes that caused errors on Linux machines.

textmineR 2.1.0

• Added vignettes for common use cases of textmineR
• Modified averaging for CalcProbCoherence
• Updated documentation to CreateTcm

textmineR 2.0.6

• Back-end changes to CreateTcm in response to new text2vec API. Functionality is unchanged.
• Changes to how the package interfaces with Rcpp

textmineR 2.0.5

• Add verbose option to CreateDtm and CreateTcm to supress status messages.
• Add function GetVocabFromDtm to get text2vec vocabulary object from a dgCMatrix document term matrix.

textmineR 2.0.4

• Patching errors introduced in version 2.0.3

textmineR 2.0.3

• Patches to CreateDtm and CreateTcm in response to updates to text2vec.
• More formal update to take advantage of text2vec’s latest optimizations to follow.

textmineR 2.0.2

• Patched CreateDtm and CreateTcm. remove_punctuation now supports non-English characters.
• Patched TmParallelApply. Added an option to declare the environment to search for your export list. Default to that argument just searches the local environment. The default should cover ~95% of use cases. (And avoids crash on Windows OS)
• Patched FitLdaModel. Use of the ... argument now allows you to control TmParallelApply, lda::lda.collapsed.gibbs.sampler, and topicmodels::LDA without error.
• Patched FitCtmModel where the ... argument now goes to topicmodels::CTM’s control argument.
• Patched CreateTcm to return objects of class dgCMatrix. This allows you to run functions like FitLdaModel on a TCM.
• Switched from irlba to RSpectra for LSA models because RSpectra’s implementation is much faster.

textmineR 2.0.1

• Patched CreateDtm and CreateTcm. An error caused stopwords to not be removed

textmineR 2.0.0

• Vec2Dtm is now deprecated in favor of CreateDtm
• A function, CreateTcm, now exists to create term co-occurrence matrices
• CreateDtm and CreateTcm are implemented with a parallel C++ back end through the text2vec library
  • the implementation is much faster! I’ve clocked 2X - 10X speedups, depending on options
  • adds external dependencies - C++ compiler and GNU make - and takes away an external dependency - Java.
  • now all tokens will be included, regardless of length. (tm’s framework silently dropped all tokens of fewer than 3 characters.)
• Allow generic stemming and stopwords in CreateDtm & CreateTcm
  • Now there is only one argument for stopwords, making it clearer how to use custom or non-English stopwords
  • Now the stemming argument allows for passing of stem/lemmatization functions.
• Function for fitting correlated topic models
• Function to turn a document term matrix to term co-occurrence matrix
• Allowed LabelTopics to use unigrams, if you want. (n-grams are still better.)
• More robust error checking for CalcTopicModelR2 and CalcLikelihood
• All function arguments use “_“, not”.”.
• CalcPhiPrime replaces (the now deprecated) GetPhiPrime
  • Allows you to pass an argument to specify non-uniform probabilities of each document
• Similarly, CalcHellingerDist and CalcJSDivergence replace HellDist and JSD. This is to conform to a naming convention where functions are “verbs”.

textmineR 1.7.0

• Added modeling capability for latent semantic analysis in FitLsaModel()
• Added CalcProbCoherence() function which replaces ProbCoherence() and can calculate probabilistic coherence for the whole phi matrix.
• Added data from NIH research grants instead of borrowed data from tm
• Removed qcq data
• Added variational em method for FitLdaModel()
• Added function to represent document clustering as a topic model Cluster2TopicModel()

textmineR 1.6.0

• Add deprecation warning to ProbCoherence
• Allow for arguments of number of cores to be passed to every function that uses implicit parallelziation
• Allow for passing of libraries to TmParallelApply (makes this function truely independent of textmineR)
• For Vec2Dtm ensure that stopwords and custom stopwords are lowercased when lower = TRUE
• Update README example to use model caches