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text2vec

Modern Text Mining Framework for R

Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.

Functions (29)

BNS

BNS

Collocations

Collocations model.

GloVe

re-export rsparse::GloVe

LatentDirichletAllocation

Creates Latent Dirichlet Allocation model.

LatentSemanticAnalysis

Latent Semantic Analysis model

RelaxedWordMoversDistance

Creates Relaxed Word Movers Distance (RWMD) model

TfIdf

TfIdf

as.lda_c

Converts document-term matrix sparse matrix to 'lda_c' format

check_analogy_accuracy

Checks accuracy of word embeddings on the analogy task

coherence

Coherence metrics for topic models

combine_vocabularies

Combines multiple vocabularies into one

create_dtm

Document-term matrix construction

create_tcm

Term-co-occurence matrix construction

create_vocabulary

Creates a vocabulary of unique terms

distances

Pairwise Distance Matrix Computation

ifiles

Creates iterator over text files from the disk

itoken

Iterators (and parallel iterators) over input objects

jsPCA_robust

(numerically robust) Dimension reduction via Jensen-Shannon Divergence & Principal Components

movie_review

IMDB movie reviews

normalize

Matrix normalization

perplexity

Perplexity of a topic model

prepare_analogy_questions

Prepares list of analogy questions

prune_vocabulary

Prune vocabulary

reexports

Objects exported from other packages

similarities

Pairwise Similarity Matrix Computation

split_into

Split a vector for parallel processing

text2vec

text2vec

tokenizers

Simple tokenization functions for string splitting

vectorizers

Vocabulary and hash vectorizers

text2vec

Modern Text Mining Framework for R

v0.6

GPL (>= 2) | file LICENSE

Authors

Dmitriy Selivanov [aut, cre, cph], Manuel Bickel [aut, cph] (Coherence measures for topic models), Qing Wang [aut, cph] (Author of the WaprLDA C++ code)

Initial release

text2vec

Functions (29)

text2vec

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