Latent Semantic Analysis model
Creates LSA(Latent semantic analysis) model. See https://en.wikipedia.org/wiki/Latent_semantic_analysis for details.
LatentSemanticAnalysis LSA
R6Class object.
For usage details see Methods, Arguments and Examples sections.
lsa = LatentSemanticAnalysis$new(n_topics) lsa$fit_transform(x, ...) lsa$transform(x, ...) lsa$components
$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
$transform(x, ...)transform new data x to latent space
A LSA object.
An input document-term matrix. Preferably in dgCMatrix format
integer desired number of latent topics.
Arguments to internal functions. Notably useful for fit_transform() -
these arguments will be passed to rsparse::soft_svd
data("movie_review")
N = 100
tokens = word_tokenizer(tolower(movie_review$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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.