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.
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 x 3
#> text book linenumber
#> <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 rowsTo 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
#> <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 rowsThis 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 rowsSentiment 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
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Sense & Sensibility 0 16.0 26.0 10.0
#> 2 Sense & Sensibility 1.00 19.0 44.0 25.0
#> 3 Sense & Sensibility 2.00 12.0 23.0 11.0
#> 4 Sense & Sensibility 3.00 15.0 22.0 7.00
#> 5 Sense & Sensibility 4.00 16.0 29.0 13.0
#> 6 Sense & Sensibility 5.00 16.0 39.0 23.0
#> 7 Sense & Sensibility 6.00 24.0 37.0 13.0
#> 8 Sense & Sensibility 7.00 22.0 39.0 17.0
#> 9 Sense & Sensibility 8.00 30.0 35.0 5.00
#> 10 Sense & Sensibility 9.00 14.0 18.0 4.00
#> # ... with 910 more rowsNow 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 x 3
#> document term count
#> <int> <chr> <dbl>
#> 1 1 adding 1.00
#> 2 1 adult 2.00
#> 3 1 ago 1.00
#> 4 1 alcohol 1.00
#> 5 1 allegedly 1.00
#> 6 1 allen 1.00
#> 7 1 apparently 2.00
#> 8 1 appeared 1.00
#> 9 1 arrested 1.00
#> 10 1 assault 1.00
#> # ... with 302,021 more rowsWe 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 0.000210 0.00000709
#> 2 abide 0.0000360 0.0000284
#> 3 abilities 0.0000360 0.000206
#> 4 ability 0.000294 0.0000213
#> 5 abroad 0.000240 0.000255
#> 6 abrupt 0.0000360 0.0000355
#> 7 absence 0.0000959 0.000787
#> 8 absent 0.0000539 0.000355
#> 9 absolute 0.0000659 0.000184
#> 10 absolutely 0.000210 0.000674
#> # ... 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.
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.