TidyText Clustering

I want to cluster words that are similar using R and the tidytext package. I have created my tokens and would now like to convert it to a matrix in order to cluster it. I would like to try out a number of token techniques to see which provides the most compact clusters.

My code is as follows (taken from the docs of widyr package). I just cant make the next step. Can anyone help?

library(janeaustenr)
library(dplyr)
library(tidytext)

# Comparing Jane Austen novels
austen_words <- austen_books() %>%
  unnest_tokens(word, text) 

# closest books to each other
closest <- austen_words %>%
  pairwise_similarity(book, word, n) %>%
  arrange(desc(similarity))

I know what to create a clustering algorithm around closest. This code will get me there but i don't know how to go from the previous section to the matrix.

d <- dist(m)
kfit <- kmeans(d, 4, nstart=100)

You can create an appropriate matrix for this via casting from tidytext. There are several functions to cast_, such as cast_sparse().

Let's use four example books, and cluster the chapters within the books:

library(tidyverse)
library(tidytext)
library(gutenbergr)
my_mirror <- "http://mirrors.xmission.com/gutenberg/"

books <- gutenberg_download(c(36, 158, 164, 345),
                            meta_fields = "title",
                            mirror = my_mirror)

books %>%
  count(title)
#> # A tibble: 4 x 2
#>   title                                     n
#> * <chr>                                 <int>
#> 1 Dracula                               15568
#> 2 Emma                                  16235
#> 3 The War of the Worlds                  6474
#> 4 Twenty Thousand Leagues under the Sea 12135

# break apart the chapters
by_chapter <- books %>%
  group_by(title) %>%
  mutate(chapter = cumsum(str_detect(text, regex("^chapter ", 
                                                 ignore_case = TRUE)))) %>%
  ungroup() %>%
  filter(chapter > 0) %>%
  unite(document, title, chapter)

glimpse(by_chapter)
#> Rows: 50,315
#> Columns: 3
#> $ gutenberg_id <int> 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, …
#> $ text         <chr> "CHAPTER ONE", "", "THE EVE OF THE WAR", "", "", "No one…
#> $ document     <chr> "The War of the Worlds_1", "The War of the Worlds_1", "T…

words_sparse <- by_chapter %>%
  unnest_tokens(word, text) %>% 
  anti_join(get_stopwords(source = "smart")) %>%
  count(document, word, sort = TRUE) %>%
  cast_sparse(document, word, n)
#> Joining, by = "word"

class(words_sparse)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"
dim(words_sparse)
#> [1]   182 18124

The words_sparse object is a sparse matrix created via cast_sparse(). You can learn more about converting back and forth from tidy and non-tidy formats for text in this chapter.

Now that you have your matrix of word counts (i.e. a document-term matrix, which you could consider weighting by tf-idf instead of counts) you can use kmeans(). How many chapters from each book were clustered together?

kfit <- kmeans(words_sparse, centers = 4)

enframe(kfit$cluster, value = "cluster") %>%
  separate(name, into = c("title", "chapter"), sep = "_") %>%
  count(title, cluster) %>%
  arrange(cluster)
#> # A tibble: 8 x 3
#>   title                                 cluster     n
#>   <chr>                                   <int> <int>
#> 1 Dracula                                     1    26
#> 2 The War of the Worlds                       1     1
#> 3 Dracula                                     2    28
#> 4 Emma                                        2     9
#> 5 The War of the Worlds                       2    26
#> 6 Twenty Thousand Leagues under the Sea       2     9
#> 7 Twenty Thousand Leagues under the Sea       3    37
#> 8 Emma                                        4    46

^{Created on 2021-02-04 by the reprex package (v1.0.0)}

One cluster is all Emma, one cluster is all Twenty Thousand Leagues under the Sea, and one cluster has chapters from all four books.

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