This project is to analyze a series of banned books by the American Library Association Compared to a non-banned book that would be considered a control. I chose the book “Frankenstein” because it was the most downloaded book over the past 30 days. I will be selecting the top 3 most banned books based on downloads from the website https://gutenberg.org/ebooks/bookshelf/422. The three most popular books are “The Adventures of Huckleberry Finn” by Mark Twain, “Ulysses” by James Joyce, and “The Awakening and other Short Stories” by Kate Chopin. I will be looking at their most common words first to look and see if there is any particular words that show off a red flag as ban worthy. Then to secure evidence I will determining the sedimentary values of the most harsh words to see if the number of harsh words are the reason for the banning of the books. Negative values are represented as -1 through -4, -4 being the lowest and harshest words. I will be only using -4 and -3 as it is most useful to see the harshest words. In order to see the rate of negative words in the books I will be taking the number of -4 and -3 words for each book and dividing them to the total number of words in the book to get a ratio.

First Ill load the packages.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(tidytext)
library(textdata)

First we look at the control.

library(readr)
Frankenstein <- read_csv("C:/Users/hamme/OneDrive/Desktop/R - Media/Text/Frankenstein.txt", 
                         col_names = FALSE)

## Warning: One or more parsing issues, see `problems()` for details

## Rows: 6387 Columns: 1
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): X1
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(Frankenstein)
Frankenstein %>% 
  unnest_tokens(word, X1) -> Frankenstein_words
Frankenstein_words %>% 
  count(word, sort =TRUE)

## # A tibble: 7,071 x 2
##    word      n
##    <chr> <int>
##  1 the    4194
##  2 and    2976
##  3 i      2846
##  4 of     2642
##  5 to     2089
##  6 my     1776
##  7 a      1391
##  8 in     1128
##  9 was    1021
## 10 that   1017
## # ... with 7,061 more rows

This shows the top ten words.

Frankenstein_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort=TRUE) %>% 
  head(10)

## Joining, by = "word"

## # A tibble: 10 x 2
##    word          n
##    <chr>     <int>
##  1 life        115
##  2 father      112
##  3 eyes        104
##  4 time         98
##  5 night        90
##  6 elizabeth    88
##  7 found        87
##  8 mind         85
##  9 heart        81
## 10 day          80

This shows the -4 words for the books

Frankenstein_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-4)) %>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Frankenstein of -4 Words" )+
  ylab("Words") + xlab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -4 words = 35

This shows the -3 words for the book.

Frankenstein_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3)) -> FR_THREE

## Joining, by = "word"

view(FR_THREE)

Frankenstein_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3)) %>% 
  head(15)%>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Frankenstein -3 Words" )+
  xlab("Words") + ylab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -3 = 639

Total Harsh Words = 674

Total Frankenstein words 674/75380= .008941 * 100 = .89% of words are harsh negatives meaning that .89 percent of the words are harsh negative words.

Now lets look at “Adventures of Huckleberry Finn”.

library(readr)
Huck <- read_csv("C:/Users/hamme/OneDrive/Desktop/R - Media/Text/Huck.txt", 
                 col_names = FALSE)

## Warning: One or more parsing issues, see `problems()` for details

## Rows: 9099 Columns: 1
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): X1
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(Huck)
Huck %>% 
  unnest_tokens(word, X1) -> Huck_words

Huck_words %>% 
  count(word, sort =TRUE)

## # A tibble: 6,595 x 2
##    word      n
##    <chr> <int>
##  1 and    6351
##  2 the    4787
##  3 i      3272
##  4 a      3148
##  5 to     2933
##  6 it     2326
##  7 was    2042
##  8 he     1657
##  9 of     1636
## 10 in     1433
## # ... with 6,585 more rows

Huck_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort=TRUE) %>% 
  head(10)

## Joining, by = "word"

## # A tibble: 10 x 2
##    word       n
##    <chr>  <int>
##  1 jim      335
##  2 time     324
##  3 warn't   290
##  4 de       252
##  5 en       235
##  6 tom      214
##  7 pretty   160
##  8 nigger   155
##  9 king     148
## 10 told     144

-4 Words

Huck_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-4))%>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Huck Finn Number of -4 Words" )+
  ylab("Words") + xlab("Number of Times used")+coord_flip()

## Joining, by = "word"

Total -4 words = 35

-3 Words

Huck_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3))-> HF_Three

## Joining, by = "word"

view(HF_Three)
Huck_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3)) %>% 
  head(15) %>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Top 15 Huck Finn -3 Words" )+
  xlab("Words") + ylab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -3 = 430

Total Harsh Words = 457

Huck Finn is 109,571 words long. 457/109,571= .00417 * 100 = .417% of words are harsh words

Now lets look at “Ulysses”.

library(readr)
Ulysses <- read_csv("C:/Users/hamme/OneDrive/Desktop/R - Media/Text/Ulysses.txt", 
                    col_names = FALSE)

## Warning: One or more parsing issues, see `problems()` for details

## Rows: 25454 Columns: 1
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): X1
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(Ulysses)
Ulysses %>% 
  unnest_tokens(word, X1) -> Ulysses_words

Ulysses_words %>% 
  count(word, sort =TRUE)

## # A tibble: 30,644 x 2
##    word      n
##    <chr> <int>
##  1 the   14902
##  2 of     8141
##  3 and    7207
##  4 a      6493
##  5 to     4954
##  6 in     4930
##  7 he     4029
##  8 his    3328
##  9 i      2680
## 10 that   2603
## # ... with 30,634 more rows

Ulysses_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort=TRUE) %>% 
  head(10)

## Joining, by = "word"

## # A tibble: 10 x 2
##    word        n
##    <chr>   <int>
##  1 bloom     933
##  2 stephen   503
##  3 time      376
##  4 eyes      329
##  5 hand      304
##  6 street    293
##  7 father    277
##  8 day       250
##  9 round     239
## 10 night     232

-4 Words

Ulysses_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-4))-> US_four

## Joining, by = "word"

view(US_four)

Ulysses_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-4)) %>%
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Ulysses Number of -4 Words" ) +
 ylab("Words") + xlab("Number of Times used") + coord_flip()

## Joining, by = "word"

Total -4 words = 187

-3 Words

Ulysses_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3))-> US_Three

## Joining, by = "word"

view(US_Three)
Ulysses_words%>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3)) %>% 
  head(15) %>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Top 15 Ulysses -3 Words" )+
  xlab("Words") + ylab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -3 = 1034

Total Harsh Words = 1221

Ulysses is 256,222 words long. 1221/256222= .00477 * 100 = .477% of words are harsh words

Now lets look at “The Awakening”.

library(readr)
Awaken <- read_csv("C:/Users/hamme/OneDrive/Desktop/R - Media/Text/Awaken.txt", 
                   col_names = FALSE)

## Warning: One or more parsing issues, see `problems()` for details

## Rows: 5834 Columns: 1
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): X1
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(Awaken)
Awaken %>% 
  unnest_tokens(word, X1) -> Awaken_words

Awaken_words %>% 
  count(word, sort =TRUE)

## # A tibble: 6,911 x 2
##    word      n
##    <chr> <int>
##  1 the    3499
##  2 and    2096
##  3 to     1814
##  4 her    1639
##  5 of     1587
##  6 she    1558
##  7 a      1464
##  8 was    1081
##  9 in      959
## 10 he      858
## # ... with 6,901 more rows

Awaken_words%>% 
  anti_join(stop_words) %>% 
  count(word, sort=TRUE) %>% 
  head(10)

## Joining, by = "word"

## # A tibble: 10 x 2
##    word           n
##    <chr>      <int>
##  1 edna         287
##  2 pontellier   180
##  3 robert       149
##  4 madame       138
##  5 day           98
##  6 house         97
##  7 time          95
##  8 don’t         94
##  9 eyes          94
## 10 night         93

-4 Words

Awaken_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-4)) %>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Awakening Number of -4 Words" )+
  ylab("Words") + xlab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -4 words = 4

-3 Words

Awaken_words %>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3))-> AW_Three

## Joining, by = "word"

view(AW_Three)
Awaken_words%>% 
  inner_join(get_sentiments('afinn')) %>% 
  count(word, value,sort = TRUE) %>% 
  arrange(desc(n)) %>% 
  filter(value == c(-3)) %>% 
  head(15) %>% 
  ggplot(aes(reorder(word,n),n, fill=word))+ geom_col()+ggtitle("Top 15 Awakening -3 Words" )+
  xlab("Words") + ylab("Number of Times used")+ coord_flip()

## Joining, by = "word"

Total -3 = 153

Total Harsh Words = 157

The Awakening is 25000 words long. 157/25000= .00628 * 100 = .628% of words are harsh words

Total Percentages

Control - Frankenstein - .89%
The Adventures of Huckleberry Finn - .417%
Ulysses - .477%
The Awakening and Other Short Stories

Conclusion

In summary there is no connection between the amount of harsh words in a book to determine how it is banned. I believe that it is because of the nature of the stories Is the reason that they were banned. Ulysses is a great example because it is clear to see why it is banned. It is because of the profanity and adult content based off of the numerous swear words but it does not have a larger proportion towards the word count.

Text Project - Banned Books

Will McCoy

3/27/2022

First we look at the control.

Total -4 words = 35

Total -3 = 639

Total Harsh Words = 674

Total Frankenstein words 674/75380= .008941 * 100 = .89% of words are harsh negatives meaning that .89 percent of the words are harsh negative words.

Now lets look at “Adventures of Huckleberry Finn”.

-4 Words

Total -4 words = 35

-3 Words

Total -3 = 430

Total Harsh Words = 457

Huck Finn is 109,571 words long. 457/109,571= .00417 * 100 = .417% of words are harsh words

Now lets look at “Ulysses”.

-4 Words

Total -4 words = 187

-3 Words

Total -3 = 1034

Total Harsh Words = 1221

Ulysses is 256,222 words long. 1221/256222= .00477 * 100 = .477% of words are harsh words

Now lets look at “The Awakening”.

-4 Words

Total -4 words = 4

-3 Words

Total -3 = 153

Total Harsh Words = 157

The Awakening is 25000 words long. 157/25000= .00628 * 100 = .628% of words are harsh words

Total Percentages

Conclusion