news.md
In ChengMengli/topic: Functions for Text Mining and Topic Modeling

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

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

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

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.

Vec2Dtm is now deprecated in favor of CreateDtm
A function, CreateTcm, now exists to create term co-occurence 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-occurence 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".

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 borrowd data from tm
Removed qcq data
Added variational em method for FitLdaModel()
Added function to represent document clustering as a topic model Cluster2TopicModel()

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

ChengMengli/topic documentation built on May 31, 2019, 8:44 p.m.

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