Tidy Topic Modeling

Topic modeling is a method for unsupervised classification of documents, by modeling each document as a mixture of topics and each topic as a mixture of words. Latent Dirichlet allocation is a particularly popular method for fitting a topic model.

We can use tidy text principles, as described in the main vignette, to approach topic modeling using consistent and effective tools. In particular, we’ll be using tidying functions for LDA objects from the topicmodels package.

Can we tell the difference between Dickens, Wells, Verne, and Austen?

Suppose a vandal has broken into your study and torn apart four of your books:

Great Expectations by Charles Dickens
The War of the Worlds by H.G. Wells
Twenty Thousand Leagues Under the Sea by Jules Verne
Pride and Prejudice by Jane Austen

This vandal has torn the books into individual chapters, and left them in one large pile. How can we restore these disorganized chapters to their original books?

Setup

library(dplyr)
library(gutenbergr)

titles <- c("Twenty Thousand Leagues under the Sea", "The War of the Worlds",
            "Pride and Prejudice", "Great Expectations")
books <- gutenberg_works(title %in% titles) %>%
  gutenberg_download(meta_fields = "title")

books

## # A tibble: 51,663 x 3
##    gutenberg_id text                                                          title                
##           <int> <chr>                                                         <chr>                
##  1           36 The War of the Worlds                                         The War of the Worlds
##  2           36 ""                                                            The War of the Worlds
##  3           36 by H. G. Wells [1898]                                         The War of the Worlds
##  4           36 ""                                                            The War of the Worlds
##  5           36 ""                                                            The War of the Worlds
##  6           36 "     But who shall dwell in these worlds if they be"         The War of the Worlds
##  7           36 "     inhabited? .  .  .  Are we or they Lords of the"        The War of the Worlds
##  8           36 "     World? .  .  .  And how are all things made for man?--" The War of the Worlds
##  9           36 "          KEPLER (quoted in The Anatomy of Melancholy)"      The War of the Worlds
## 10           36 ""                                                            The War of the Worlds
## # ... with 51,653 more rows

As pre-processing, we divide these into chapters, use tidytext’s unnest_tokens to separate them into words, then remove stop_words. We’re treating every chapter as a separate “document”, each with a name like Great Expectations_1 or Pride and Prejudice_11.

library(tidytext)
library(stringr)
library(tidyr)

by_chapter <- books %>%
  group_by(title) %>%
  mutate(chapter = cumsum(str_detect(text, regex("^chapter ", ignore_case = TRUE)))) %>%
  ungroup() %>%
  filter(chapter > 0)

by_chapter_word <- by_chapter %>%
  unite(title_chapter, title, chapter) %>%
  unnest_tokens(word, text)

word_counts <- by_chapter_word %>%
  anti_join(stop_words) %>%
  count(title_chapter, word, sort = TRUE) %>%
  ungroup()

word_counts

## # A tibble: 104,721 x 3
##    title_chapter            word        n
##    <chr>                    <chr>   <int>
##  1 Great Expectations_57    joe        88
##  2 Great Expectations_7     joe        70
##  3 Great Expectations_17    biddy      63
##  4 Great Expectations_27    joe        58
##  5 Great Expectations_38    estella    58
##  6 Great Expectations_2     joe        56
##  7 Great Expectations_23    pocket     53
##  8 Great Expectations_15    joe        50
##  9 Great Expectations_18    joe        50
## 10 The War of the Worlds_16 brother    50
## # ... with 104,711 more rows

Latent Dirichlet Allocation with the topicmodels package

Right now this data frame is in a tidy form, with one-term-per-document-per-row. However, the topicmodels package requires a DocumentTermMatrix (from the tm package). As described in this vignette, we can cast a one-token-per-row table into a DocumentTermMatrix with tidytext’s cast_dtm:

chapters_dtm <- word_counts %>%
  cast_dtm(title_chapter, word, n)

chapters_dtm

## <<DocumentTermMatrix (documents: 193, terms: 18215)>>
## Non-/sparse entries: 104721/3410774
## Sparsity           : 97%
## Maximal term length: 19
## Weighting          : term frequency (tf)

Now we are ready to use the topicmodels package to create a four topic LDA model.

library(topicmodels)
chapters_lda <- LDA(chapters_dtm, k = 4, control = list(seed = 1234))
chapters_lda

## A LDA_VEM topic model with 4 topics.

(In this case we know there are four topics because there are four books; in practice we may need to try a few different values of k).

Now tidytext gives us the option of returning to a tidy analysis, using the tidy and augment verbs borrowed from the broom package. In particular, we start with the tidy verb.

chapters_lda_td <- tidy(chapters_lda)
chapters_lda_td

## # A tibble: 72,860 x 3
##    topic term                                                                                 beta
##    <int> <chr>                                                                               <dbl>
##  1     1 joe     0.000000000000000000000248                                                       
##  2     2 joe     0.0000000000000000000000000000000000000000000000000000000000000000874            
##  3     3 joe     0.000000000000000000000000112                                                    
##  4     4 joe     0.0142                                                                           
##  5     1 biddy   0.000000000000000000000000000000322                                              
##  6     2 biddy   0.0000000000000000000000000000000000000000000000000000000000000000000000000000631
##  7     3 biddy   0.0000000000000000000000000000000000000000000000000276                           
##  8     4 biddy   0.00469                                                                          
##  9     1 estella 0.000000185                                                                      
## 10     2 estella 0.000000000000000000000000000000000000000000000000000000000000000000000000349    
## # ... with 72,850 more rows

Notice that this has turned the model into a one-topic-per-term-per-row format. For each combination the model has \(\beta\), the probability of that term being generated from that topic.

We could use dplyr’s top_n to find the top 5 terms within each topic:

top_terms <- chapters_lda_td %>%
  group_by(topic) %>%
  top_n(5, beta) %>%
  ungroup() %>%
  arrange(topic, -beta)

top_terms

## # A tibble: 20 x 3
##    topic term         beta
##    <int> <chr>       <dbl>
##  1     1 elizabeth 0.0144 
##  2     1 darcy     0.00900
##  3     1 miss      0.00874
##  4     1 bennet    0.00709
##  5     1 jane      0.00665
##  6     2 captain   0.0155 
##  7     2 nautilus  0.0131 
##  8     2 sea       0.00885
##  9     2 nemo      0.00872
## 10     2 ned       0.00804
## 11     3 people    0.00678
## 12     3 martians  0.00649
## 13     3 time      0.00535
## 14     3 black     0.00526
## 15     3 night     0.00450
## 16     4 joe       0.0142 
## 17     4 time      0.00682
## 18     4 pip       0.00670
## 19     4 looked    0.00632
## 20     4 miss      0.00625

This model lends itself to a visualization:

library(ggplot2)
theme_set(theme_bw())

top_terms %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ topic, scales = "free") +
  theme(axis.text.x = element_text(size = 15, angle = 90, hjust = 1))

These topics are pretty clearly associated with the four books! There’s no question that the topic of “nemo”, “sea”, and “nautilus” belongs to Twenty Thousand Leagues Under the Sea, and that “jane”, “darcy”, and “elizabeth” belongs to Pride and Prejudice. We see “pip” and “joe” from Great Expectations and “martians”, “black”, and “night” from The War of the Worlds.

Per-document classification

Each chapter was a “document” in this analysis. Thus, we may want to know which topics are associated with each document. Can we put the chapters back together in the correct books?

chapters_lda_gamma <- tidy(chapters_lda, matrix = "gamma")
chapters_lda_gamma

## # A tibble: 772 x 3
##    document                 topic      gamma
##    <chr>                    <int>      <dbl>
##  1 Great Expectations_57        1 0.0000118 
##  2 Great Expectations_7         1 0.0000129 
##  3 Great Expectations_17        1 0.0000185 
##  4 Great Expectations_27        1 0.0000168 
##  5 Great Expectations_38        1 0.342     
##  6 Great Expectations_2         1 0.0000151 
##  7 Great Expectations_23        1 0.533     
##  8 Great Expectations_15        1 0.0000126 
##  9 Great Expectations_18        1 0.0000111 
## 10 The War of the Worlds_16     1 0.00000949
## # ... with 762 more rows

Setting matrix = "gamma" returns a tidied version with one-document-per-topic-per-row. Now that we have these document classifications, we can see how well our unsupervised learning did at distinguishing the four books. First we re-separate the document name into title and chapter:

chapters_lda_gamma <- chapters_lda_gamma %>%
  separate(document, c("title", "chapter"), sep = "_", convert = TRUE)
chapters_lda_gamma

## # A tibble: 772 x 4
##    title                 chapter topic      gamma
##    <chr>                   <int> <int>      <dbl>
##  1 Great Expectations         57     1 0.0000118 
##  2 Great Expectations          7     1 0.0000129 
##  3 Great Expectations         17     1 0.0000185 
##  4 Great Expectations         27     1 0.0000168 
##  5 Great Expectations         38     1 0.342     
##  6 Great Expectations          2     1 0.0000151 
##  7 Great Expectations         23     1 0.533     
##  8 Great Expectations         15     1 0.0000126 
##  9 Great Expectations         18     1 0.0000111 
## 10 The War of the Worlds      16     1 0.00000949
## # ... with 762 more rows

Then we examine what fraction of chapters we got right for each:

ggplot(chapters_lda_gamma, aes(gamma, fill = factor(topic))) +
  geom_histogram() +
  facet_wrap(~ title, nrow = 2)

We notice that almost all of the chapters from Pride and Prejudice, War of the Worlds, and Twenty Thousand Leagues Under the Sea were uniquely identified as a single topic each.

chapter_classifications <- chapters_lda_gamma %>%
  group_by(title, chapter) %>%
  top_n(1, gamma) %>%
  ungroup() %>%
  arrange(gamma)

chapter_classifications

## # A tibble: 193 x 4
##    title              chapter topic gamma
##    <chr>                <int> <int> <dbl>
##  1 Great Expectations      54     3 0.508
##  2 Great Expectations      23     1 0.533
##  3 Great Expectations      22     4 0.543
##  4 Great Expectations      31     4 0.549
##  5 Great Expectations      33     4 0.590
##  6 Great Expectations      56     4 0.594
##  7 Great Expectations      47     4 0.629
##  8 Great Expectations      38     4 0.658
##  9 Great Expectations      11     4 0.677
## 10 Great Expectations      44     4 0.696
## # ... with 183 more rows

We can determine this by finding the consensus book for each, which we note is correct based on our earlier visualization:

book_topics <- chapter_classifications %>%
  count(title, topic) %>%
  top_n(1, n) %>%
  ungroup() %>%
  transmute(consensus = title, topic)

book_topics

## # A tibble: 1 x 2
##   consensus           topic
##   <chr>               <int>
## 1 Pride and Prejudice     1

Then we see which chapters were misidentified:

chapter_classifications %>%
  inner_join(book_topics, by = "topic") %>%
  count(title, consensus)

## # A tibble: 2 x 3
##   title               consensus               n
##   <chr>               <chr>               <int>
## 1 Great Expectations  Pride and Prejudice     1
## 2 Pride and Prejudice Pride and Prejudice    61

We see that only a few chapters from Great Expectations were misclassified. Not bad for unsupervised clustering!

By word assignments: `augment`

One important step in the topic modeling expectation-maximization algorithm is assigning each word in each document to a topic. The more words in a document are assigned to that topic, generally, the more weight (gamma) will go on that document-topic classification.

We may want to take the original document-word pairs and find which words in each document were assigned to which topic. This is the job of the augment verb.

assignments <- augment(chapters_lda, data = chapters_dtm)

We can combine this with the consensus book titles to find which words were incorrectly classified.

assignments <- assignments %>%
  separate(document, c("title", "chapter"), sep = "_", convert = TRUE) %>%
  inner_join(book_topics, by = c(".topic" = "topic"))

assignments

## # A tibble: 28,863 x 6
##    title               chapter term    count .topic consensus          
##    <chr>                 <int> <chr>   <dbl>  <dbl> <chr>              
##  1 Pride and Prejudice      47 pocket   1.00   1.00 Pride and Prejudice
##  2 Pride and Prejudice      50 pocket   1.00   1.00 Pride and Prejudice
##  3 Pride and Prejudice      49 pocket   1.00   1.00 Pride and Prejudice
##  4 Great Expectations       38 brother  2.00   1.00 Pride and Prejudice
##  5 Pride and Prejudice      43 brother  4.00   1.00 Pride and Prejudice
##  6 Pride and Prejudice      18 brother  1.00   1.00 Pride and Prejudice
##  7 Great Expectations       22 brother  4.00   1.00 Pride and Prejudice
##  8 Pride and Prejudice      45 brother  2.00   1.00 Pride and Prejudice
##  9 Pride and Prejudice      16 brother  1.00   1.00 Pride and Prejudice
## 10 Pride and Prejudice      10 brother  2.00   1.00 Pride and Prejudice
## # ... with 28,853 more rows

We can, for example, create a “confusion matrix” using dplyr’s count and tidyr’s spread:

assignments %>%
  count(title, consensus, wt = count) %>%
  spread(consensus, n, fill = 0)

## # A tibble: 3 x 2
##   title                                 `Pride and Prejudice`
##   <chr>                                                 <dbl>
## 1 Great Expectations                                  3222   
## 2 Pride and Prejudice                                37234   
## 3 Twenty Thousand Leagues under the Sea                  5.00

We notice that almost all the words for Pride and Prejudice, Twenty Thousand Leagues Under the Sea, and War of the Worlds were correctly assigned, while Great Expectations had a fair amount of misassignment.

What were the most commonly mistaken words?

wrong_words <- assignments %>%
  filter(title != consensus)

wrong_words

## # A tibble: 2,588 x 6
##    title                                 chapter term    count .topic consensus          
##    <chr>                                   <int> <chr>   <dbl>  <dbl> <chr>              
##  1 Great Expectations                         38 brother  2.00   1.00 Pride and Prejudice
##  2 Great Expectations                         22 brother  4.00   1.00 Pride and Prejudice
##  3 Great Expectations                         23 miss     2.00   1.00 Pride and Prejudice
##  4 Great Expectations                         22 miss    23.0    1.00 Pride and Prejudice
##  5 Twenty Thousand Leagues under the Sea       8 miss     1.00   1.00 Pride and Prejudice
##  6 Great Expectations                         31 miss     1.00   1.00 Pride and Prejudice
##  7 Great Expectations                         22 people   3.00   1.00 Pride and Prejudice
##  8 Great Expectations                         31 people   2.00   1.00 Pride and Prejudice
##  9 Great Expectations                         23 dear    10.0    1.00 Pride and Prejudice
## 10 Great Expectations                         38 love     7.00   1.00 Pride and Prejudice
## # ... with 2,578 more rows

wrong_words %>%
  count(title, consensus, term, wt = count) %>%
  ungroup() %>%
  arrange(desc(n))

## # A tibble: 1,946 x 4
##    title              consensus           term          n
##    <chr>              <chr>               <chr>     <dbl>
##  1 Great Expectations Pride and Prejudice love       42.0
##  2 Great Expectations Pride and Prejudice lady       29.0
##  3 Great Expectations Pride and Prejudice miss       26.0
##  4 Great Expectations Pride and Prejudice father     19.0
##  5 Great Expectations Pride and Prejudice flopson    18.0
##  6 Great Expectations Pride and Prejudice family     15.0
##  7 Great Expectations Pride and Prejudice speak      14.0
##  8 Great Expectations Pride and Prejudice attention  13.0
##  9 Great Expectations Pride and Prejudice children   13.0
## 10 Great Expectations Pride and Prejudice jane       13.0
## # ... with 1,936 more rows

Notice the word “flopson” here; these wrong words do not necessarily appear in the novels they were misassigned to. Indeed, we can confirm “flopson” appears only in Great Expectations:

word_counts %>%
  filter(word == "flopson")

## # A tibble: 3 x 3
##   title_chapter         word        n
##   <chr>                 <chr>   <int>
## 1 Great Expectations_22 flopson    10
## 2 Great Expectations_23 flopson     7
## 3 Great Expectations_33 flopson     1

The algorithm is stochastic and iterative, and it can accidentally land on a topic that spans multiple books.

Tidy Topic Modeling

Julia Silge and David Robinson

2018-02-18

Can we tell the difference between Dickens, Wells, Verne, and Austen?

Setup

Latent Dirichlet Allocation with the topicmodels package

Per-document classification

By word assignments: `augment`

Tidy Topic Modeling

Julia Silge and David Robinson

2018-02-18

Can we tell the difference between Dickens, Wells, Verne, and Austen?

Setup

Latent Dirichlet Allocation with the topicmodels package

Per-document classification

By word assignments: augment

By word assignments: `augment`