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
#> Source: local data frame [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
#> .. ... ... ...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
#> Source: local data frame [725,054 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
#> .. ... ... ...This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, ngrams, 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)
#> Source: local data frame [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
#> .. ... ...Sentiment analysis can be done as an inner join. Three sentiment lexicons are in the tidytext package in the sentiment dataset. Let's examine how sentiment changes 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)
bing <- sentiments %>%
filter(lexicon == "bing") %>%
select(-score)
bing
#> Source: local data frame [6,788 x 3]
#>
#> word sentiment lexicon
#> <chr> <chr> <chr>
#> 1 2-faced negative bing
#> 2 2-faces negative bing
#> 3 a+ positive bing
#> 4 abnormal negative bing
#> 5 abolish negative bing
#> 6 abominable negative bing
#> 7 abominably negative bing
#> 8 abominate negative bing
#> 9 abomination negative bing
#> 10 abort negative bing
#> .. ... ... ...
janeaustensentiment <- tidy_books %>%
inner_join(bing) %>%
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
#> .. ... ... ... ... ...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)
#> Source: local data frame [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
#> .. ... ... ...We could find the most negative documents:
ap_sentiments <- tidy(AssociatedPress) %>%
inner_join(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
#> Source: local data frame [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
#> .. ... ... ...
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.
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.