# Text Mining with gutenbergr and tidytext

This vignette demonstrates a complete text mining workflow using gutenbergr and tidytext. We’ll perform an in-depth analysis of Jane Austen’s Persuasion, exploring its vocabulary, sentiment, structure, and themes. See Text Mining with R for a great introduction to text mining.

## Required Libraries

    library(dplyr)
    library(tidytext)
    library(ggplot2)
    library(tidyr)
    library(stringr)

## Download the Book

First, let’s find and download Jane Austen’s Persuasion:

    gutenberg_works(str_detect(title, "Persuasion"))

    #> # A tibble: 2 × 8
    #>   gutenberg_id title     author gutenberg_author_id language gutenberg_bookshelf
    #>          <int> <chr>     <chr>                <int> <fct>    <chr>
    #> 1          105 Persuasi… Auste…                  68 en       Category: Novels/C…
    #> 2        56582 The Gent… Gray,…               48927 en       Category: British …
    #> # ℹ 2 more variables: rights <fct>, has_text <lgl>

We can see there are multiple works returned. 105 is Persuasion:

    persuasion <- gutenberg_download(105, meta_fields = "title")

    persuasion

    #> # A tibble: 8,357 × 3
    #>    gutenberg_id text             title
    #>           <int> <chr>            <chr>
    #>  1          105 "Persuasion"     Persuasion
    #>  2          105 ""               Persuasion
    #>  3          105 ""               Persuasion
    #>  4          105 "by Jane Austen" Persuasion
    #>  5          105 ""               Persuasion
    #>  6          105 "(1818)"         Persuasion
    #>  7          105 ""               Persuasion
    #>  8          105 ""               Persuasion
    #>  9          105 ""               Persuasion
    #> 10          105 ""               Persuasion
    #> # ℹ 8,347 more rows

## Structural Analysis: Adding Chapters

Project Gutenberg texts processed into tibbles of lines. To analyze the book’s progression, we’ll use `gutenberg_add_sections()`. This function identifies headers and fills them down to create a structural column.

    persuasion <- persuasion |>
      gutenberg_add_sections(
        pattern = "^Chapter [IVXLCDM]+",
        section_col = "chapter",
        format_fn = function(x) {
          x |>
            str_remove("^CHAPTER\\s+") |>
            str_remove("\\.$") |>
            as.roman() |>
            as.numeric()
        }
      )

    # Preview the new structure
    persuasion |>
      filter(!is.na(chapter)) |>
      head()

    #> # A tibble: 6 × 4
    #>   gutenberg_id text                                                title chapter
    #>          <int> <chr>                                               <chr>   <dbl>
    #> 1          105 "CHAPTER I."                                        Pers…       1
    #> 2          105 ""                                                  Pers…       1
    #> 3          105 ""                                                  Pers…       1
    #> 4          105 "Sir Walter Elliot, of Kellynch Hall, in Somersets… Pers…       1
    #> 5          105 "for his own amusement, never took up any book but… Pers…       1
    #> 6          105 "he found occupation for an idle hour, and consola… Pers…       1

## Tokenization

We need to move from a one-row-per-line format to a one-row-per-token format. We’ll use `tidytext::unnest_tokens()` to split the text into individual words and remove stop words `tidytext::stop_words`.

    words <- persuasion |>
      unnest_tokens(word, text) |>
      anti_join(stop_words, by = "word")

## Word Frequency Analysis

Tokenization makes it trivial to find the most frequent words in the text:

    word_counts <- words |>
      count(word, sort = TRUE)

    word_counts

    #> # A tibble: 5,384 × 2
    #>    word          n
    #>    <chr>     <int>
    #>  1 anne        447
    #>  2 captain     302
    #>  3 elliot      254
    #>  4 lady        214
    #>  5 wentworth   191
    #>  6 charles     155
    #>  7 time        152
    #>  8 sir         149
    #>  9 miss        125
    #> 10 walter      123
    #> # ℹ 5,374 more rows

Let’s visualize the top 20 words:

    word_counts |>
      slice_max(n, n = 20) |>
      mutate(word = reorder(word, n)) |>
      ggplot(aes(x = n, y = word, fill = word)) +
      geom_col(show.legend = FALSE) +
      labs(
        title = expression(paste("Most Common Words in ", italic("Persuasion"))),
        x = "Frequency",
        y = NULL
      ) +
      theme_minimal()

Character names (Anne, Captain, Elliot, Wentworth) dominate the most frequent words, which makes sense for a character-driven novel.

## Sentiment Analysis

Natural language processing uses sentiment analysis to identify emotive/affective states.

### Overall Sentiment

Let’s use the NRC sentiment lexicon, which classifies words into categories like “joy”, “trust”, “fear”, and “sadness”. This will allow us to view the overall sentiment of the book.

> Note: The NRC lexicon requires accepting a license agreement during installation and is only free for non-commercial use. The code below shows the analysis workflow, with pre-computed results displayed.

    nrc_sentiments <- get_sentiments("nrc")

    word_sentiments <- words |>
      inner_join(nrc_sentiments, by = "word", relationship = "many-to-many") |>
      count(sentiment, sort = TRUE)

Visualize the distribution of sentiments:

    word_sentiments |>
      mutate(sentiment = reorder(sentiment, n)) |>
      ggplot(aes(x = n, y = sentiment, fill = sentiment)) +
      geom_col(show.legend = FALSE) +
      labs(
        title = expression(paste(
          "Sentiment Distribution in ",
          italic("Persuasion")
        )),
        x = "Word Count",
        y = NULL
      ) +
      theme_minimal()

Bar chart showing the distribution of NRC sentiment categories across all words in Persuasion.

### By Chapter

We can aggregate these sentiments by the chapter structure we created earlier.

    nrc_by_chapter <- words |>
      inner_join(nrc_sentiments, by = "word", relationship = "many-to-many") |>
      count(chapter, sentiment) |>
      filter(!is.na(chapter))

    nrc_by_chapter |>
      filter(sentiment %in% c("joy", "sadness", "anger", "fear")) |>
      ggplot(aes(x = chapter, y = n, fill = factor(sentiment))) +
      geom_col(show.legend = FALSE) +
      facet_wrap(~sentiment, ncol = 2, scales = "free_y") +
      labs(
        title = expression(paste("Sentiment by Chapter in ", italic("Persuasion"))),
        x = "Chapter",
        y = "Word Count"
      ) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text = element_text(face = "bold")
      )

Four faceted bar charts showing counts of joy, sadness, anger, and fear words per chapter in Persuasion.

### Sentiment Progression

We can also see the general emotive content as the book progresses by dividing the text into bins of words to track how specific emotions fluctuate across the narrative arc.

For good measure, let’s add another x-axis with chapter labels so we can correlate the sentiment with portions of the narrative.

    # Add a running index to preserve order and calculate bins
    words_with_index <- words |>
      mutate(word_index = row_number()) |>
      mutate(bin = (word_index - 1) %/% 500 + 1)

    nrc_binned <- words_with_index |>
      inner_join(nrc_sentiments, by = "word", relationship = "many-to-many") |>
      count(bin, sentiment)

    # Add labels for chapters
    chapter_breaks <- words |>
      filter(!is.na(chapter)) |>
      mutate(word_index = row_number()) |>
      group_by(chapter) |>
      slice_min(word_index, n = 1) |>
      ungroup() |>
      mutate(
        bin = (word_index - 1) %/% 500 + 1
      ) |>
      filter(chapter %% 2 == 0)

    nrc_binned |>
      filter(sentiment %in% c("joy", "sadness", "anger", "fear")) |>
      ggplot(aes(x = bin, y = n, color = sentiment)) +
      geom_line(linewidth = 1, show.legend = FALSE) +
      facet_wrap(~sentiment, ncol = 2, scales = "free_y") +
      scale_x_continuous(
        name = "Word Bin (500 words)",
        sec.axis = sec_axis(
          ~.,
          breaks = chapter_breaks$bin,
          labels = chapter_breaks$chapter,
          name = "Chapter"
        )
      ) +
      labs(
        title = expression(paste(
          "Sentiment Progression in ",
          italic("Persuasion")
        )),
        subtitle = "NRC sentiments by word bin with chapter reference",
        y = "Word Count"
      ) +
      theme_minimal()

Four line charts tracking joy, sadness, anger, and fear word counts across 500-word bins in Persuasion, with a secondary x-axis showing even chapter numbers.

## TF-IDF: Finding Unique Chapter Words

While simple frequency tells us who the main characters are, TF-IDF, or term frequency–inverse document frequency, tells us which words are most important to a specific chapter relative to the rest of the corpus. This is excellent for identifying specific plot points or settings (like the move to Bath or the trip to Lyme).

    chapter_words <- persuasion |>
      unnest_tokens(word, text) |>
      count(chapter, word, sort = TRUE) |>
      bind_tf_idf(word, chapter, n)

    # Look at the most "important" words for chapters 10 through 13
    chapter_words |>
      filter(chapter %in% 10:13) |>
      group_by(chapter) |>
      slice_max(tf_idf, n = 5) |>
      ungroup() |>
      mutate(word = reorder(word, tf_idf)) |>
      ggplot(aes(tf_idf, word, fill = factor(chapter))) +
      geom_col(show.legend = FALSE) +
      facet_wrap(~chapter, scales = "free") +
      labs(
        title = "Highest TF-IDF words in Chapters 10-13",
        x = "TF-IDF",
        y = NULL
      ) +
      theme_minimal()

Highest TF-IDF words for chapters 10–13, showing the most chapter-distinctive terms relative to the rest of the novel.