Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools

Text mining for word processing and sentiment analysis using 'dplyr', 'ggplot2', and other tidy tools.


Authors: Julia Silge, David Robinson
License: MIT

Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like dplyr, broom, tidyr and ggplot2. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.

You can install this package from CRAN:

install.packages("tidytext")

Or you can install the development version from Github with devtools:

library(devtools)
install_github("juliasilge/tidytext")

The novels of Jane Austen can be so tidy! Let's use the text of Jane Austen's 6 completed, published novels from the janeaustenr package, and bring them into a tidy format. janeaustenr provides them as a one-row-per-line format:

library(janeaustenr)
library(dplyr)
 
original_books <- austen_books() %>%
  group_by(book) %>%
  mutate(linenumber = row_number()) %>%
  ungroup()
 
original_books
#> # A tibble: 73,422 × 3
#>                     text                book linenumber
#>                    <chr>              <fctr>      <int>
#> 1  SENSE AND SENSIBILITY Sense & Sensibility          1
#> 2                        Sense & Sensibility          2
#> 3         by Jane Austen Sense & Sensibility          3
#> 4                        Sense & Sensibility          4
#> 5                 (1811) Sense & Sensibility          5
#> 6                        Sense & Sensibility          6
#> 7                        Sense & Sensibility          7
#> 8                        Sense & Sensibility          8
#> 9                        Sense & Sensibility          9
#> 10             CHAPTER 1 Sense & Sensibility         10
#> # ... with 73,412 more rows

To work with this as a tidy dataset, we need to restructure it as one-token-per-row format. The unnest_tokens function is a way to convert a dataframe with a text column to be one-token-per-row:

library(tidytext)
tidy_books <- original_books %>%
  unnest_tokens(word, text)
 
tidy_books
#> # A tibble: 725,054 × 3
#>                   book linenumber        word
#>                 <fctr>      <int>       <chr>
#> 1  Sense & Sensibility          1       sense
#> 2  Sense & Sensibility          1         and
#> 3  Sense & Sensibility          1 sensibility
#> 4  Sense & Sensibility          3          by
#> 5  Sense & Sensibility          3        jane
#> 6  Sense & Sensibility          3      austen
#> 7  Sense & Sensibility          5        1811
#> 8  Sense & Sensibility         10     chapter
#> 9  Sense & Sensibility         10           1
#> 10 Sense & Sensibility         13         the
#> # ... with 725,044 more rows

This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, n-grams, sentences, lines, paragraphs, or separation around a regex pattern.

Now that the data is in one-word-per-row format, we can manipulate it with tidy tools like dplyr. We can remove stop words (kept in the tidytext dataset stop_words) with an anti_join.

data("stop_words")
tidy_books <- tidy_books %>%
  anti_join(stop_words)

We can also use count to find the most common words in all the books as a whole.

tidy_books %>%
  count(word, sort = TRUE) 
#> # A tibble: 13,914 × 2
#>      word     n
#>     <chr> <int>
#> 1    miss  1855
#> 2    time  1337
#> 3   fanny   862
#> 4    dear   822
#> 5    lady   817
#> 6     sir   806
#> 7     day   797
#> 8    emma   787
#> 9  sister   727
#> 10  house   699
#> # ... with 13,904 more rows

Sentiment analysis can be done as an inner join. Three sentiment lexicons are available via the get_sentiments() function. Let's examine how sentiment changes during each novel. Let's find a sentiment score for each word using the Bing lexicon, then count the number of positive and negative words in defined sections of each novel.

library(tidyr)
get_sentiments("bing")
#> # A tibble: 6,788 × 2
#>           word sentiment
#>          <chr>     <chr>
#> 1      2-faced  negative
#> 2      2-faces  negative
#> 3           a+  positive
#> 4     abnormal  negative
#> 5      abolish  negative
#> 6   abominable  negative
#> 7   abominably  negative
#> 8    abominate  negative
#> 9  abomination  negative
#> 10       abort  negative
#> # ... with 6,778 more rows
 
janeaustensentiment <- tidy_books %>%
  inner_join(get_sentiments("bing"), by = "word") %>% 
  count(book, index = linenumber %/% 80, sentiment) %>% 
  spread(sentiment, n, fill = 0) %>% 
  mutate(sentiment = positive - negative)
 
janeaustensentiment
#> Source: local data frame [920 x 5]
#> Groups: book, index [920]
#> 
#>                   book index negative positive sentiment
#>                 <fctr> <dbl>    <dbl>    <dbl>     <dbl>
#> 1  Sense & Sensibility     0       16       26        10
#> 2  Sense & Sensibility     1       19       44        25
#> 3  Sense & Sensibility     2       12       23        11
#> 4  Sense & Sensibility     3       15       22         7
#> 5  Sense & Sensibility     4       16       29        13
#> 6  Sense & Sensibility     5       16       39        23
#> 7  Sense & Sensibility     6       24       37        13
#> 8  Sense & Sensibility     7       22       39        17
#> 9  Sense & Sensibility     8       30       35         5
#> 10 Sense & Sensibility     9       14       18         4
#> # ... with 910 more rows

Now we can plot these sentiment scores across the plot trajectory of each novel.

library(ggplot2)
 
ggplot(janeaustensentiment, aes(index, sentiment, fill = book)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = "free_x")

For more examples of text mining using tidy data frames, see the tidytext vignette.

Many existing text mining datasets are in the form of a DocumentTermMatrix class (from the tm package). For example, consider the corpus of 2246 Associated Press articles from the topicmodels dataset.

library(tm)
data("AssociatedPress", package = "topicmodels")
AssociatedPress
#> <<DocumentTermMatrix (documents: 2246, terms: 10473)>>
#> Non-/sparse entries: 302031/23220327
#> Sparsity           : 99%
#> Maximal term length: 18
#> Weighting          : term frequency (tf)

If we want to analyze this with tidy tools, we need to transform it into a one-row-per-term data frame first with a tidy function. (For more on the tidy verb, see the broom package).

tidy(AssociatedPress)
#> # A tibble: 302,031 × 3
#>    document       term count
#>       <int>      <chr> <dbl>
#> 1         1     adding     1
#> 2         1      adult     2
#> 3         1        ago     1
#> 4         1    alcohol     1
#> 5         1  allegedly     1
#> 6         1      allen     1
#> 7         1 apparently     2
#> 8         1   appeared     1
#> 9         1   arrested     1
#> 10        1    assault     1
#> # ... with 302,021 more rows

We could find the most negative documents:

ap_sentiments <- tidy(AssociatedPress) %>%
  inner_join(get_sentiments("bing"), by = c(term = "word")) %>%
  count(document, sentiment, wt = count) %>%
  ungroup() %>%
  spread(sentiment, n, fill = 0) %>%
  mutate(sentiment = positive - negative) %>%
  arrange(sentiment)

Or we can join the Austen and AP datasets and compare the frequencies of each word:

comparison <- tidy(AssociatedPress) %>%
  count(word = term) %>%
  rename(AP = n) %>%
  inner_join(count(tidy_books, word)) %>%
  rename(Austen = n) %>%
  mutate(AP = AP / sum(AP),
         Austen = Austen / sum(Austen))
 
comparison
#> # A tibble: 4,437 × 3
#>          word           AP       Austen
#>         <chr>        <dbl>        <dbl>
#> 1   abandoned 2.097944e-04 7.093959e-06
#> 2       abide 3.596475e-05 2.837584e-05
#> 3   abilities 3.596475e-05 2.057248e-04
#> 4     ability 2.937122e-04 2.128188e-05
#> 5      abroad 2.397650e-04 2.553825e-04
#> 6      abrupt 3.596475e-05 3.546980e-05
#> 7     absence 9.590601e-05 7.874295e-04
#> 8      absent 5.394713e-05 3.546980e-04
#> 9    absolute 6.593538e-05 1.844429e-04
#> 10 absolutely 2.097944e-04 6.739262e-04
#> # ... with 4,427 more rows
 
library(scales)
ggplot(comparison, aes(AP, Austen)) +
  geom_point() +
  geom_text(aes(label = word), check_overlap = TRUE,
            vjust = 1, hjust = 1) +
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  geom_abline(color = "red")

For more examples of working with objects from other text mining packages using tidy data principles, see the vignette on converting to and from document term matrices.

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. Feedback, bug reports (and fixes!), and feature requests are welcome; file issues or seek support here.

News

tidytext 0.1.2

  • Updated DESCRIPTION to require purrr >= 0.1.1.
  • Fixed cast_sparse, cast_dtm, and other sparse casters to ignore groups in the input (#19)
  • Changed unnest_tokens so that it no longer uses tidyr's unnest, but rather a custom version that removes some overhead. In some experiments, this sped up unnest_tokens on large inputs by about 40%. This also moves tidyr from Imports to Suggests for now.
  • unnest_tokens now checks that there are no list columns in the input, and raises an error if present (since those cannot be unnested).
  • Added a format argument to unnest_tokens so that it can process html, xml, latex or man pages using the hunspell package, though only when token = "words".
  • Added a get_sentiments function that takes the name of a lexicon ("nrc", "bing", or "sentiment") and returns just that sentiment data frame (#25)

tidytext 0.1.1

  • Added documentation for n-grams, skip n-grams, and regex
  • Added codecov and appveyor
  • Added tidiers for LDA objects from topicmodels and a vignette on topic modeling
  • Added function to calculate tf-idf of a tidy text dataset and a tf-idf vignette
  • Fixed a bug when tidying by line/sentence/paragraph/regex and there are multiple non-text columns
  • Fixed a bug when unnesting using n-grams and skip n-grams (entire text was not being collapsed)
  • Added ability to pass a (custom tokenizing) function to token. Also added a collapse argument that makes the choice whether to combine lines before tokenizing explicit.
  • Changed tidy.dictionary to return a tbl_df rather than a data.frame
  • Updated cast_sparse to work with dplyr 0.5.0
  • Deprecated the pair_count function, which has been moved to pairwise_count in the widyr package. This will be removed entirely in a future version.

tidytext 0.1.0

  • Initial release for text mining using tidy tools

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("tidytext")

0.1.2 by Julia Silge, 7 months ago


http://github.com/juliasilge/tidytext


Report a bug at http://github.com/juliasilge/tidytext/issues


Browse source code at https://github.com/cran/tidytext


Authors: Gabriela De Queiroz [ctb], David Robinson [aut], Julia Silge [aut, cre]


Documentation:   PDF Manual  


Task views: Natural Language Processing


MIT + file LICENSE license


Imports dplyr, stringr, hunspell, broom, Matrix, tokenizers, janeaustenr, purrr, methods

Suggests readr, tidyr, XML, tm, wordcloud, knitr, rmarkdown, ggplot2, reshape2, topicmodels, quanteda, NLP, scales, gutenbergr, testthat


Imported by statquotes.

Suggested by fivethirtyeight, gutenbergr.


See at CRAN