Learn R Programming

⚠️There's a newer version (0.4.2) of this package.Take me there.

tidytext: Text mining 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.

Installation

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")

Tidy text mining example: the unnest_tokens function

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 x 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: 725,055 x 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 725,045 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 x 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 x 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
#> # A tibble: 920 x 5
#>                   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.

Tidying document term matrices

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 x 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 x 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(alpha = 0.5) +
  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.

Community Guidelines

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.

Copy Link

Version

Install

install.packages('tidytext')

Monthly Downloads

35,709

Version

0.1.5

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

November 18th, 2017

Functions in tidytext (0.1.5)

lda_tidiers

Tidiers for LDA objects from the topicmodels package
mallet_tidiers

Tidiers for Latent Dirichlet Allocation models from the mallet package
dictionary_tidiers

Tidy dictionary objects from the quanteda package
corpus_tidiers

Tidiers for a corpus object from the quanteda package
parts_of_speech

Parts of speech for English words from the Moby Project
bind_tf_idf_

Deprecated SE version of functions
bind_tf_idf

Bind the term frequency and inverse document frequency of a tidy text dataset to the dataset
stm_tidiers

Tidiers for Structural Topic Models from the stm package
cast_sparse

Create a sparse matrix from row names, column names, and values in a table.
stop_words

Various lexicons for English stop words
cast_tdm

Casting a data frame to a DocumentTermMatrix, TermDocumentMatrix, or dfm
tdm_tidiers

Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package
reexports

Objects exported from other packages
tidy.Corpus

Tidy a Corpus object from the tm package
sentiments

Sentiment lexicons from three sources
unnest_tokens

Split a column into tokens using the tokenizers package
tidy_triplet

Utility function to tidy a simple triplet matrix
tidytext

tidytext: Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
get_sentiments

Get a tidy data frame of a single sentiment lexicon