R: tidytext: tidytext

# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd

# The tidy text format {#tidytext}
library(knitr)
opts_chunk$set(message = FALSE, warning = FALSE, cache = TRUE)
options(width = 100, dplyr.width = 100)
library(ggplot2)
theme_set(theme_light())

## The `unnest_tokens` function
text <- c("Because I could not stop for Death -",
          "He kindly stopped for me -",
          "The Carriage held but just Ourselves -",
          "and Immortality")
text

library(dplyr)
text_df <- tibble(line = 1:4, text = text)
text_df

# Within our tidy text framework, we need to both break the text
# into individual tokens (a process called *tokenization*) *and*
# transform it to a tidy data structure.
# To do this, we use tidytext's `unnest_tokens()` function.
library(tidytext)
text_df %>%
  unnest_tokens(word, text)

## Tidying the works of Jane Austen {#tidyausten}
library(janeaustenr)
library(dplyr)
library(stringr)
original_books <- austen_books() %>%
  group_by(book) %>%
  mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
                                                 ignore_case = TRUE)))) %>%
  ungroup()
original_books

# To work with this as a tidy dataset,
# we need to restructure it in the **one-token-per-row** format,
# which as we saw earlier is done with the `unnest_tokens()` function.
library(tidytext)
tidy_books <- original_books %>%
  unnest_tokens(word, text)
tidy_books

# Now that the data is in one-word-per-row format,
# we can manipulate it with tidy tools like dplyr.
# Often in text analysis, we will want to remove stop words;
# stop words are words that are not useful for an analysis,
# typically extremely common words such as "the", "of", "to", and so forth in English.
# 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 dplyr's `count()` to find the most common words in all the books
# as a whole.
tidy_books %>%
  count(word, sort = TRUE) 

# Because we've been using tidy tools, our word counts are stored in a tidy data frame.
# This allows us to pipe this directly to the ggplot2 package,
# for example to create a visualization of the most common words
# (Figure \@ref(fig:plotcount)).
library(ggplot2)
tidy_books %>%
  count(word, sort = TRUE) %>%
  filter(n > 600) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip()

Referensi

• https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd

Pranala Menarik

• R