Tidy Topic Modeling

Julia Silge and David Robinson

2017-06-19

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:

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 2.479998e-22
##  2     2     joe 8.740236e-65
##  3     3     joe 1.122876e-25
##  4     4     joe 1.421187e-02
##  5     1   biddy 3.217760e-31
##  6     2   biddy 6.307844e-77
##  7     3   biddy 2.764079e-50
##  8     4   biddy 4.689302e-03
##  9     1 estella 1.850492e-07
## 10     2 estella 3.488596e-73
## # ... 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 0.014400661
##  2     1     darcy 0.008997398
##  3     1      miss 0.008737032
##  4     1    bennet 0.007091783
##  5     1      jane 0.006646326
##  6     2   captain 0.015518496
##  7     2  nautilus 0.013061053
##  8     2       sea 0.008845536
##  9     2      nemo 0.008715741
## 10     2       ned 0.008037571
## 11     3    people 0.006780434
## 12     3  martians 0.006488534
## 13     3      time 0.005348928
## 14     3     black 0.005255630
## 15     3     night 0.004497131
## 16     4       joe 0.014211874
## 17     4      time 0.006817358
## 18     4       pip 0.006704879
## 19     4    looked 0.006319436
## 20     4      miss 0.006253280

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 1.183472e-05
##  2     Great Expectations_7     1 1.287508e-05
##  3    Great Expectations_17     1 1.853388e-05
##  4    Great Expectations_27     1 1.680594e-05
##  5    Great Expectations_38     1 3.420291e-01
##  6     Great Expectations_2     1 1.508989e-05
##  7    Great Expectations_23     1 5.333548e-01
##  8    Great Expectations_15     1 1.258855e-05
##  9    Great Expectations_18     1 1.113576e-05
## 10 The War of the Worlds_16     1 9.492523e-06
## # ... 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 1.183472e-05
##  2    Great Expectations       7     1 1.287508e-05
##  3    Great Expectations      17     1 1.853388e-05
##  4    Great Expectations      27     1 1.680594e-05
##  5    Great Expectations      38     1 3.420291e-01
##  6    Great Expectations       2     1 1.508989e-05
##  7    Great Expectations      23     1 5.333548e-01
##  8    Great Expectations      15     1 1.258855e-05
##  9    Great Expectations      18     1 1.113576e-05
## 10 The War of the Worlds      16     1 9.492523e-06
## # ... 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 0.5078902
##  2 Great Expectations      23     1 0.5333548
##  3 Great Expectations      22     4 0.5432274
##  4 Great Expectations      31     4 0.5490885
##  5 Great Expectations      33     4 0.5895732
##  6 Great Expectations      56     4 0.5940343
##  7 Great Expectations      47     4 0.6290204
##  8 Great Expectations      38     4 0.6579485
##  9 Great Expectations      11     4 0.6769217
## 10 Great Expectations      44     4 0.6962118
## # ... 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      1 Pride and Prejudice
##  2 Pride and Prejudice      50  pocket     1      1 Pride and Prejudice
##  3 Pride and Prejudice      49  pocket     1      1 Pride and Prejudice
##  4  Great Expectations      38 brother     2      1 Pride and Prejudice
##  5 Pride and Prejudice      43 brother     4      1 Pride and Prejudice
##  6 Pride and Prejudice      18 brother     1      1 Pride and Prejudice
##  7  Great Expectations      22 brother     4      1 Pride and Prejudice
##  8 Pride and Prejudice      45 brother     2      1 Pride and Prejudice
##  9 Pride and Prejudice      16 brother     1      1 Pride and Prejudice
## 10 Pride and Prejudice      10 brother     2      1 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

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      1 Pride and Prejudice
##  2                    Great Expectations      22 brother     4      1 Pride and Prejudice
##  3                    Great Expectations      23    miss     2      1 Pride and Prejudice
##  4                    Great Expectations      22    miss    23      1 Pride and Prejudice
##  5 Twenty Thousand Leagues under the Sea       8    miss     1      1 Pride and Prejudice
##  6                    Great Expectations      31    miss     1      1 Pride and Prejudice
##  7                    Great Expectations      22  people     3      1 Pride and Prejudice
##  8                    Great Expectations      31  people     2      1 Pride and Prejudice
##  9                    Great Expectations      23    dear    10      1 Pride and Prejudice
## 10                    Great Expectations      38    love     7      1 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
##  2 Great Expectations Pride and Prejudice      lady    29
##  3 Great Expectations Pride and Prejudice      miss    26
##  4 Great Expectations Pride and Prejudice    father    19
##  5 Great Expectations Pride and Prejudice   flopson    18
##  6 Great Expectations Pride and Prejudice    family    15
##  7 Great Expectations Pride and Prejudice     speak    14
##  8 Great Expectations Pride and Prejudice attention    13
##  9 Great Expectations Pride and Prejudice  children    13
## 10 Great Expectations Pride and Prejudice      jane    13
## # ... 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.