LatentSemanticAnalysis {text2vec}

Latent Semantic Analysis model

Description

Creates LSA(Latent semantic analysis) model. See https://en.wikipedia.org/wiki/Latent_semantic_analysis for details.

Usage

LatentSemanticAnalysis

LSA

Format

R6Class object.

Usage

For usage details see Methods, Arguments and Examples sections.

lsa = LatentSemanticAnalysis$new(n_topics)
lsa$fit_transform(x, ...)
lsa$transform(x, ...)
lsa$components

Methods

$new(n_topics)

create LSA model with n_topics latent topics

$fit_transform(x, ...)

fit model to an input sparse matrix (preferably in dgCMatrix format) and then transform x to latent space

$transform(x, ...)

transform new data x to latent space

Arguments

lsa

A LSA object.

x

An input document-term matrix. Preferably in dgCMatrix format

n_topics

integer desired number of latent topics.

...

Arguments to internal functions. Notably useful for fit_transform() - these arguments will be passed to rsparse::soft_svd

Examples

data("movie_review")
N = 100
tokens = word_tokenizer(tolower(movie_review$review[1:N]))
dtm = create_dtm(itoken(tokens), hash_vectorizer(2**10))
n_topics = 5
lsa_1 = LatentSemanticAnalysis$new(n_topics)
d1 = lsa_1$fit_transform(dtm)
# the same, but wrapped with S3 methods
d2 = fit_transform(dtm, lsa_1)