In news-r/gensimr: Topic Modelling

knitr::opts_chunk$set(
  warning = FALSE,
  collapse = TRUE,
  comment = "#>"
)

par(bg = '#f9f7f1')

library(htmltools)
reticulate::use_virtualenv("./env")

br()
br()

div(
  class = "row",
  div(
    class = "col-md-4",
    img(
      src = "logo.png",
      class = "img-responsive responsive-img"
    )
  ),
  div(
    class = "col-md-8",
    p(
      "Topic Modeling for Humans with gensim.",
      br(),
      "Large scale efficient topic modeling in R and Python."
    ),
    br(),
    p(
      tags$a(
        tags$i(class = "fa fa-level-down-alt"),
        class = "btn btn-primary",
        href = "articles/installation.html",
        style = "margin-bottom: 5px;",
        "Installation"
      ),
      tags$a(
        tags$i(class = "fa fa-terminal"),
        class = "btn btn-default",
        href = "reference",
        style = "margin-bottom: 5px;",
        "Reference"
      )
    )
  )
)

Example

Below we build a very basic Latent Dirichlet Allocation model aiming for 2 latent dimensions using example data.

library(gensimr)

# example corpus
data("corpus", package = "gensimr")

# preprocess documents
texts <- prepare_documents(corpus)
dictionary <- corpora_dictionary(texts)
corpus <- doc2bow(dictionary, texts)

# create tf-idf model
tfidf <- model_tfidf(corpus)
tfidf_corpus <- wrap(tfidf, corpus)

# latent similarity index
lda <- model_lda(tfidf_corpus, id2word = dictionary, num_topics = 2L)
topics <- lda$print_topics() # get topics

Objects returned by the package are not automatically converted to R data structures, use reticulate::py_to_r as shown below to convert them.

reticulate::py_to_r(topics) # convert to R format

We can then use our model to transform our corpus and then the document topic matrix.

corpus_wrapped <- wrap(lda, corpus)
doc_topics <- get_docs_topics(corpus_wrapped)
plot(doc_topics$dimension_1_y, doc_topics$dimension_2_y)

corpus_wrapped <- wrap(lda, corpus)
doc_topics <- get_docs_topics(corpus_wrapped)
par(bg = '#f4f1e6')
plot(doc_topics$dimension_1_y, doc_topics$dimension_2_y)

The plot correctly identifies two topics/clusters. As stated in table 2 from this paper, the example corpus (data(corpus)) essentially has two classes of documents. First five are about human-computer interaction and the other four are about graphs.

news-r/gensimr documentation built on Jan. 9, 2021, 5:55 a.m.