All functions

CalcGamma()

Calculate a matrix whose rows represent P(topic_i|tokens)

CalcHellingerDist()

Calculate Hellinger Distance

CalcJSDivergence()

Calculate Jensen-Shannon Divergence

CalcLikelihood()

Calculate the log likelihood of a document term matrix given a topic model

CalcProbCoherence()

Probabilistic coherence of topics

CalcTopicModelR2()

Calculate the R-squared of a topic model.

Cluster2TopicModel()

Represent a document clustering as a topic model

CreateDtm()

Convert a character vector to a document term matrix.

CreateTcm()

Convert a character vector to a term co-occurrence matrix.

Dtm2Docs()

Convert a DTM to a Character Vector of documents

Dtm2Lexicon()

Turn a document term matrix into a list for LDA Gibbs sampling

Dtm2Tcm()

Turn a document term matrix into a term co-occurrence matrix

FitCtmModel()

Fit a Correlated Topic Model

FitLdaModel()

Fit a Latent Dirichlet Allocation topic model

FitLsaModel()

Fit a topic model using Latent Semantic Analysis

GetProbableTerms()

Get cluster labels using a "more probable" method of terms

GetTopTerms()

Get Top Terms for each topic from a topic model

Internals

Internal helper functions for textmineR

LabelTopics()

Get some topic labels using a "more probable" method of terms

SummarizeTopics()

Summarize topics in a topic model

TermDocFreq()

Get term frequencies and document frequencies from a document term matrix.

TmParallelApply()

An OS-independent parallel version of lapply

nih_sample nih_sample_dtm nih_sample_topic_model

Abstracts and metadata from NIH research grants awarded in 2014

posterior()

Posterior methods for topic models

posterior(<lda_topic_model>)

Draw from the posterior of an LDA topic model

predict(<ctm_topic_model>)

Predict method for Correlated topic models (CTM)

predict(<lda_topic_model>)

Get predictions from a Latent Dirichlet Allocation model

predict(<lsa_topic_model>)

Predict method for LSA topic models

textmineR

textmineR

update()

Update methods for topic models

update(<lda_topic_model>)

Update a Latent Dirichlet Allocation topic model with new data