Functions for Text Mining and Topic Modeling
Calculate a matrix whose rows represent P(topic_i|tokens)
Calculate Hellinger Distance
Calculate Jensen-Shannon Divergence
Calculate the log likelihood of a document term matrix given a topic m...
Probabilistic coherence of topics
Calculate the R-squared of a topic model.
Represent a document clustering as a topic model
Convert a character vector to a document term matrix.
Convert a character vector to a term co-occurrence matrix.
Convert a DTM to a Character Vector of documents
Turn a document term matrix into a list for LDA Gibbs sampling
Turn a document term matrix into a term co-occurrence matrix
Fit a Correlated Topic Model
Fit a Latent Dirichlet Allocation topic model
Fit a topic model using Latent Semantic Analysis
Get cluster labels using a "more probable" method of terms
Get Top Terms for each topic from a topic model
Internal helper functions for textmineR
Get some topic labels using a "more probable" method of terms
Abstracts and metadata from NIH research grants awarded in 2014
Draw from the posterior of an LDA topic model
Posterior methods for topic models
Predict method for Correlated topic models (CTM)
Get predictions from a Latent Dirichlet Allocation model
Predict method for LSA topic models
Summarize topics in a topic model
Get term frequencies and document frequencies from a document term mat...
Deprecated functions in package textmineR.
textmineR
An OS-independent parallel version of lapply
Update a Latent Dirichlet Allocation topic model with new data
Update methods for topic models
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.