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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. Check out our book to learn more about text mining using tidy data principles.

Installation

You can install this package from CRAN:

install.packages("tidytext")

Or you can install the development version from GitHub with remotes:

library(remotes)
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(line = row_number()) %>%
  ungroup()

original_books
#> # A tibble: 73,422 x 3
#>    text                  book                 line
#>    <chr>                 <fct>               <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                 line word       
#>    <fct>               <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 (available via the function get_stopwords()) with an anti_join.

tidy_books <- tidy_books %>%
  anti_join(get_stopwords())

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: 14,375 x 2
#>    word      n
#>    <chr> <int>
#>  1 mr     3015
#>  2 mrs    2446
#>  3 must   2071
#>  4 said   2041
#>  5 much   1935
#>  6 miss   1855
#>  7 one    1831
#>  8 well   1523
#>  9 every  1456
#> 10 think  1440
#> # … with 14,365 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: 6,786 x 2
#>    word        sentiment
#>    <chr>       <chr>    
#>  1 2-faces     negative 
#>  2 abnormal    negative 
#>  3 abolish     negative 
#>  4 abominable  negative 
#>  5 abominably  negative 
#>  6 abominate   negative 
#>  7 abomination negative 
#>  8 abort       negative 
#>  9 aborted     negative 
#> 10 aborts      negative 
#> # … with 6,776 more rows

janeaustensentiment <- tidy_books %>%
  inner_join(get_sentiments("bing"), by = "word") %>% 
  count(book, index = line %/% 80, sentiment) %>% 
  spread(sentiment, n, fill = 0) %>% 
  mutate(sentiment = positive - negative)

janeaustensentiment
#> # A tibble: 920 x 5
#>    book                index negative positive sentiment
#>    <fct>               <dbl>    <dbl>    <dbl>     <dbl>
#>  1 Sense & Sensibility     0       16       32        16
#>  2 Sense & Sensibility     1       19       53        34
#>  3 Sense & Sensibility     2       12       31        19
#>  4 Sense & Sensibility     3       15       31        16
#>  5 Sense & Sensibility     4       16       34        18
#>  6 Sense & Sensibility     5       16       51        35
#>  7 Sense & Sensibility     6       24       40        16
#>  8 Sense & Sensibility     7       23       51        28
#>  9 Sense & Sensibility     8       30       40        10
#> 10 Sense & Sensibility     9       15       19         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) %>%
  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: 4,730 x 3
#>    word             AP     Austen
#>    <chr>         <dbl>      <dbl>
#>  1 abandoned 0.000170  0.00000493
#>  2 abide     0.0000291 0.0000197 
#>  3 abilities 0.0000291 0.000143  
#>  4 ability   0.000238  0.0000148 
#>  5 able      0.000664  0.00151   
#>  6 abroad    0.000194  0.000178  
#>  7 abrupt    0.0000291 0.0000247 
#>  8 absence   0.0000776 0.000547  
#>  9 absent    0.0000436 0.000247  
#> 10 absolute  0.0000533 0.000128  
#> # … with 4,720 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.

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Install

install.packages('tidytext')

Monthly Downloads

35,709

Version

0.2.2

License

MIT + file LICENSE

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Last Published

July 29th, 2019

Functions in tidytext (0.2.2)

stop_words

Various lexicons for English stop words
stm_tidiers

Tidiers for Structural Topic Models from the stm package
sentiments

Sentiment lexicon from Bing Liu and collaborators
tidy.Corpus

Tidy a Corpus object from the tm package
tidytext

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

Utility function to tidy a simple triplet matrix
unnest_tokens

Split a column into tokens using the tokenizers package
cast_tdm

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

English negators, modals, and adverbs
parts_of_speech

Parts of speech for English words from the Moby Project
tdm_tidiers

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

Objects exported from other packages
reorder_within

Reorder an x or y axis within facets
dictionary_tidiers

Tidy dictionary objects from the quanteda package
cast_sparse

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

Get a tidy data frame of a single sentiment lexicon
bind_tf_idf_

Deprecated SE version of functions
lda_tidiers

Tidiers for LDA and CTM objects from the topicmodels package
get_stopwords

Get a tidy data frame of a single stopword lexicon
corpus_tidiers

Tidiers for a corpus object from the quanteda package
bind_tf_idf

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

Tidiers for Latent Dirichlet Allocation models from the mallet package