Week10Assignment_ZMO
Zainab.O
2023-11-04
#The function get_sentiments() allows us to get specific sentiment lexicons with the appropriate measures for each one.
#install.packages("afinn")
#install.packages("bing")
#install.packages("nrc")#I had to download library(“textdata”) because I could not install afinn,bing or nrc seperately
#install.packages("textdata")library(tidytext)
get_sentiments("afinn")## # A tibble: 2,477 × 2
## word value
## <chr> <dbl>
## 1 abandon -2
## 2 abandoned -2
## 3 abandons -2
## 4 abducted -2
## 5 abduction -2
## 6 abductions -2
## 7 abhor -3
## 8 abhorred -3
## 9 abhorrent -3
## 10 abhors -3
## # ℹ 2,467 more rowsget_sentiments("bing")## # A tibble: 6,786 × 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # ℹ 6,776 more rowsget_sentiments("nrc")## # A tibble: 13,872 × 2
## word sentiment
## <chr> <chr>
## 1 abacus trust
## 2 abandon fear
## 3 abandon negative
## 4 abandon sadness
## 5 abandoned anger
## 6 abandoned fear
## 7 abandoned negative
## 8 abandoned sadness
## 9 abandonment anger
## 10 abandonment fear
## # ℹ 13,862 more rowsTitle:Sentiment analysis with inner join
#Let’s look at the words with a joy score from the NRC lexicon. = The
ACTION.What we are doing #What are the most common joy words in Emma? =
THE Question #The parts are broken down as such: First, we need to take
the text of the novels and convert the text to the tidy format using
unnest_tokens(), just as we did in Section 1.3.
# Let’s also set up some other columns to keep track of which line and
chapter of the book each word comes from; we use group_by and mutate to
construct those columns.
library(janeaustenr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
tidy_books <- austen_books() %>%
group_by(book) %>%
mutate(
linenumber = row_number(),
chapter = cumsum(str_detect(text,
regex("^chapter [\\divxlc]",
ignore_case = TRUE)))) %>%
ungroup() %>%
unnest_tokens(word, text)WHAT IS HAPPENING #we chose the name word for the output column from unnest_tokens(). This is a convenient choice because the sentiment lexicons and stop word datasets have columns named word; performing inner joins and anti-joins is thus easier.
#Now that the text is in a tidy format with one word per row, we are ready to do the sentiment analysis. #First, let’s use the NRC lexicon and filter() for the joy words. #Next, let’s filter() the data frame with the text from the books for the words from Emma and then use inner_join() to perform the sentiment analysis. #What are the most common joy words in Emma? Let’s use count() from dplyr.
nrc_joy <- get_sentiments("nrc") %>%
filter(sentiment == "joy")
tidy_books %>%
filter(book == "Emma") %>%
inner_join(nrc_joy) %>%
count(word, sort = TRUE)## Joining with `by = join_by(word)`## # A tibble: 301 × 2
## word n
## <chr> <int>
## 1 good 359
## 2 friend 166
## 3 hope 143
## 4 happy 125
## 5 love 117
## 6 deal 92
## 7 found 92
## 8 present 89
## 9 kind 82
## 10 happiness 76
## # ℹ 291 more rows#We can also examine how sentiment changes throughout each novel. We can do this with just a handful of lines that are mostly dplyr functions. #First, we find a sentiment score for each word using the Bing lexicon and inner_join().
#Next, we count up how many positive and negative words there are in defined sections of each book. #We define an index here to keep track of where we are in the narrative; this index (using integer division) counts up sections of 80 lines of text. #. We then use pivot_wider() so that we have negative and positive sentiment in separate columns, and lastly calculate a net sentiment (positive - negative).
library(tidyr)
jane_austen_sentiment <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(book, index = linenumber %/% 80, sentiment) %>%
pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>%
mutate(sentiment = positive - negative)## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 435434 of `x` matches multiple rows in `y`.
## ℹ Row 5051 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.#Now we can plot these sentiment scores across the plot trajectory of each novel. #Notice that we are plotting against the index on the x-axis that keeps track of narrative time in sections of text.
library(ggplot2)
ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
Title:Comparing the three sentiment dictionaries First, let’s use filter() to choose only the words from the one novel we are interested in.
pride_prejudice <- tidy_books %>%
filter(book == "Pride & Prejudice")pride_prejudice## # A tibble: 122,204 × 4
## book linenumber chapter word
## <fct> <int> <int> <chr>
## 1 Pride & Prejudice 1 0 pride
## 2 Pride & Prejudice 1 0 and
## 3 Pride & Prejudice 1 0 prejudice
## 4 Pride & Prejudice 3 0 by
## 5 Pride & Prejudice 3 0 jane
## 6 Pride & Prejudice 3 0 austen
## 7 Pride & Prejudice 7 1 chapter
## 8 Pride & Prejudice 7 1 1
## 9 Pride & Prejudice 10 1 it
## 10 Pride & Prejudice 10 1 is
## # ℹ 122,194 more rows#Now, we can use inner_join() to calculate the sentiment in different ways. #Let’s again use integer division (%/%) to define larger sections of text that span multiple lines, #and we can use the same pattern with count(), pivot_wider(), and mutate() to find the net sentiment in each of these sections of text.
afinn <- pride_prejudice %>%
inner_join(get_sentiments("afinn")) %>%
group_by(index = linenumber %/% 80) %>%
summarise(sentiment = sum(value)) %>%
mutate(method = "AFINN")## Joining with `by = join_by(word)`bing_and_nrc <- bind_rows(
pride_prejudice %>%
inner_join(get_sentiments("bing")) %>%
mutate(method = "Bing et al."),
pride_prejudice %>%
inner_join(get_sentiments("nrc") %>%
filter(sentiment %in% c("positive",
"negative"))
) %>%
mutate(method = "NRC")) %>%
count(method, index = linenumber %/% 80, sentiment) %>%
pivot_wider(names_from = sentiment,
values_from = n,
values_fill = 0) %>%
mutate(sentiment = positive - negative)## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("nrc") %>% filter(sentiment %in% : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 215 of `x` matches multiple rows in `y`.
## ℹ Row 5178 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.#We now have an estimate of the net sentiment (positive - negative) in each chunk of the novel text for each sentiment lexicon. Let’s bind them together and visualize them
bind_rows(afinn,
bing_and_nrc) %>%
ggplot(aes(index, sentiment, fill = method)) +
geom_col(show.legend = FALSE) +
facet_wrap(~method, ncol = 1, scales = "free_y")
Why is, for example, the result for the NRC lexicon biased so high in sentiment compared to the Bing et al. result? Let’s look briefly at how many positive and negative words are in these lexicons.
get_sentiments("nrc") %>%
filter(sentiment %in% c("positive", "negative")) %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 3316
## 2 positive 2308get_sentiments("bing") %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 2005Most common positive and negative words THE ACTION: analyze word counts that contribute to each sentiment. By implementing count() here with arguments of both word and sentiment, we find out how much each word contributed to each sentiment.
bing_word_counts <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 435434 of `x` matches multiple rows in `y`.
## ℹ Row 5051 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.bing_word_counts## # A tibble: 2,585 × 3
## word sentiment n
## <chr> <chr> <int>
## 1 miss negative 1855
## 2 well positive 1523
## 3 good positive 1380
## 4 great positive 981
## 5 like positive 725
## 6 better positive 639
## 7 enough positive 613
## 8 happy positive 534
## 9 love positive 495
## 10 pleasure positive 462
## # ℹ 2,575 more rows#This can be shown visually, an
bing_word_counts %>%
group_by(sentiment) %>%
slice_max(n, n = 10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(n, word, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(x = "Contribution to sentiment",
y = NULL)
WordCloud
library(wordcloud)## Loading required package: RColorBrewertidy_books %>%
anti_join(stop_words) %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))## Joining with `by = join_by(word)`
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithstidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = c("gray20", "gray80"),
max.words = 100)## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 435434 of `x` matches multiple rows in `y`.
## ℹ Row 5051 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
#some sentiment analysis algorithms look beyond only unigrams (i.e. single words) to try to understand the sentiment of a sentence as a whole. These algorithms try to understand that
#I am not having a good day.
#is a sad sentence, not a happy one
For these, we may want to tokenize text into sentences, and it makes sense to use a new name for the output column in such a case.
p_and_p_sentences <- tibble(text = prideprejudice) %>%
unnest_tokens(sentence, text, token = "sentences")p_and_p_sentences$sentence[2]## [1] "by jane austen"#Another option in unnest_tokens() is to split into tokens using a regex pattern. We could use this, for example, to split the text of Jane Austen’s novels into a data frame by chapter.
austen_chapters <- austen_books() %>%
group_by(book) %>%
unnest_tokens(chapter, text, token = "regex",
pattern = "Chapter|CHAPTER [\\dIVXLC]") %>%
ungroup()
austen_chapters %>%
group_by(book) %>%
summarise(chapters = n())## # A tibble: 6 × 2
## book chapters
## <fct> <int>
## 1 Sense & Sensibility 51
## 2 Pride & Prejudice 62
## 3 Mansfield Park 49
## 4 Emma 56
## 5 Northanger Abbey 32
## 6 Persuasion 25WHAT HAPPENED: We have recovered the correct number of chapters in each novel (plus an “extra” row for each novel title). In the austen_chapters data frame, each row corresponds to one chapter.
WHAT ARE WE LOOKING: We can use tidy text analysis to ask questions such as what are the most negative chapters in each of Jane Austen’s novels? First, let’s get the list of negative words from the Bing lexicon. Second, let’s make a data frame of how many words are in each chapter so we can normalize for the length of chapters.
bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")wordcounts <- tidy_books %>%
group_by(book, chapter) %>%
summarize(words = n())## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.Then, let’s find the number of negative words in each chapter and divide by the total words in each chapter. For each book, which chapter has the highest proportion of negative words?
tidy_books %>%
semi_join(bingnegative) %>%
group_by(book, chapter) %>%
summarize(negativewords = n()) %>%
left_join(wordcounts, by = c("book", "chapter")) %>%
mutate(ratio = negativewords/words) %>%
filter(chapter != 0) %>%
slice_max(ratio, n = 1) %>%
ungroup()## Joining with `by = join_by(word)`
## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.## # A tibble: 6 × 5
## book chapter negativewords words ratio
## <fct> <int> <int> <int> <dbl>
## 1 Sense & Sensibility 43 161 3405 0.0473
## 2 Pride & Prejudice 34 111 2104 0.0528
## 3 Mansfield Park 46 173 3685 0.0469
## 4 Emma 15 151 3340 0.0452
## 5 Northanger Abbey 21 149 2982 0.0500
## 6 Persuasion 4 62 1807 0.0343I want to check something out
austen_books()## # A tibble: 73,422 × 2
## text book
## * <chr> <fct>
## 1 "SENSE AND SENSIBILITY" Sense & Sensibility
## 2 "" Sense & Sensibility
## 3 "by Jane Austen" Sense & Sensibility
## 4 "" Sense & Sensibility
## 5 "(1811)" Sense & Sensibility
## 6 "" Sense & Sensibility
## 7 "" Sense & Sensibility
## 8 "" Sense & Sensibility
## 9 "" Sense & Sensibility
## 10 "CHAPTER 1" Sense & Sensibility
## # ℹ 73,412 more rowsMY EXTENSION: I found a Rpackage for H.C.Anderson Fairytales and I am going to use loughran sentiments. I could not find the several other lexicons, just loughran.
THE NEW CORPUS
#install.packages("hcandersenr")
library(hcandersenr)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()hca_fairytales() %>%
select(book, language) %>%
unique() %>%
mutate(langauge = fct_relevel(language, c("English", "Spanish", "German", "Danish", "French"))) %>%
ggplot(aes(langauge, book)) +
geom_raster(alpha = 0.3) +
scale_x_discrete(position = "top")
READ IN THE DATA The Book I need : English
hcandersen_en## # A tibble: 31,380 × 2
## text book
## <chr> <chr>
## 1 "A soldier came marching along the high road: \"Left, right - left, ri… The …
## 2 "had his knapsack on his back, and a sword at his side; he had been to… The …
## 3 "and was now returning home. As he walked on, he met a very frightful-… The …
## 4 "witch in the road. Her under-lip hung quite down on her breast, and s… The …
## 5 "and said, \"Good evening, soldier; you have a very fine sword, and a … The …
## 6 "knapsack, and you are a real soldier; so you shall have as much money… The …
## 7 "you like.\"" The …
## 8 "\"Thank you, old witch,\" said the soldier." The …
## 9 "\"Do you see that large tree,\" said the witch, pointing to a tree wh… The …
## 10 "beside them. \"Well, it is quite hollow inside, and you must climb to… The …
## # ℹ 31,370 more rowsloughran_sentiments = get_sentiments("loughran")
loughran_sentiments## # A tibble: 4,150 × 2
## word sentiment
## <chr> <chr>
## 1 abandon negative
## 2 abandoned negative
## 3 abandoning negative
## 4 abandonment negative
## 5 abandonments negative
## 6 abandons negative
## 7 abdicated negative
## 8 abdicates negative
## 9 abdicating negative
## 10 abdication negative
## # ℹ 4,140 more rowstidy_books_ft = hcandersen_en %>%
group_by(book) %>%
mutate(
linenumber = row_number()
)%>%
ungroup() %>%
unnest_tokens(word,text)I picked Little Claus and Big Claus book beacuse 1)we are close to the holiday ,2) it is one of the longer books and 3) because the snow Queen is not apart of the english package
Claus = tidy_books_ft %>%
filter(book == "Little Claus and Big Claus")
Claus## # A tibble: 0 × 3
## # ℹ 3 variables: book <chr>, linenumber <int>, word <chr>tidy_books_ft## # A tibble: 416,311 × 3
## book linenumber word
## <chr> <int> <chr>
## 1 The tinder-box 1 a
## 2 The tinder-box 1 soldier
## 3 The tinder-box 1 came
## 4 The tinder-box 1 marching
## 5 The tinder-box 1 along
## 6 The tinder-box 1 the
## 7 The tinder-box 1 high
## 8 The tinder-box 1 road
## 9 The tinder-box 1 left
## 10 The tinder-box 1 right
## # ℹ 416,301 more rowsTHIS ONLY GOES UP TO TINDER BOX. I did take out the ungroup function, did not make a difference I put in line number and took it out , it does not make a difference Since the en version is a database, let me try two different things
Claus = hcandersen_en %>%
filter(book == "Little Claus and Big Claus")
Claus## # A tibble: 0 × 2
## # ℹ 2 variables: text <chr>, book <chr>I have to read this in using a the package hcandersenr and the Tidy data frame hca fairytales which already has three colmun: text, Book and language and filter out from there
tidy_books_tales = hcandersenr::hca_fairytales() %>%
group_by(book) %>%
ungroup() %>%
unnest_tokens(word, text)tidy_books_tales## # A tibble: 1,567,749 × 3
## book language word
## <chr> <chr> <chr>
## 1 The tinder-box Danish der
## 2 The tinder-box Danish kom
## 3 The tinder-box Danish en
## 4 The tinder-box Danish soldat
## 5 The tinder-box Danish marcherende
## 6 The tinder-box Danish hen
## 7 The tinder-box Danish ad
## 8 The tinder-box Danish landevejen
## 9 The tinder-box Danish én
## 10 The tinder-box Danish to
## # ℹ 1,567,739 more rowsClausv2 = tidy_books_tales %>%
filter(book == "Litte Claus and big claus" ) %>%
filter(language == "English")
Clausv2## # A tibble: 0 × 3
## # ℹ 3 variables: book <chr>, language <chr>, word <chr>New Story :The Tinder-box
Tinder = tidy_books_tales %>%
filter(book == "The tinder-box") %>%
filter(language == "English")
Tinder## # A tibble: 2,908 × 3
## book language word
## <chr> <chr> <chr>
## 1 The tinder-box English a
## 2 The tinder-box English soldier
## 3 The tinder-box English came
## 4 The tinder-box English marching
## 5 The tinder-box English along
## 6 The tinder-box English the
## 7 The tinder-box English high
## 8 The tinder-box English road
## 9 The tinder-box English left
## 10 The tinder-box English right
## # ℹ 2,898 more rowsANALYSIS
afinn_tales = Tinder %>%
inner_join(get_sentiments("afinn")) %>%
summarise(sentiment = sum(value)) %>%
mutate(method = "AFINN")## Joining with `by = join_by(word)`loughran_tales = Tinder %>%
inner_join(get_sentiments("loughran")) %>%
filter(!is.na(sentiment)) %>%
count(sentiment, sort = TRUE)## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("loughran")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2656 of `x` matches multiple rows in `y`.
## ℹ Row 2526 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.loughran_tales## # A tibble: 5 × 2
## sentiment n
## <chr> <int>
## 1 negative 24
## 2 positive 24
## 3 uncertainty 15
## 4 litigious 7
## 5 constraining 2loughran_counts = Tinder %>%
inner_join(get_sentiments("loughran")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("loughran")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2656 of `x` matches multiple rows in `y`.
## ℹ Row 2526 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.loughran_counts## # A tibble: 35 × 3
## word sentiment n
## <chr> <chr> <int>
## 1 could uncertainty 9
## 2 good positive 6
## 3 shall litigious 5
## 4 cut negative 4
## 5 great positive 4
## 6 might uncertainty 4
## 7 beautiful positive 3
## 8 best positive 3
## 9 pleasant positive 3
## 10 closed negative 2
## # ℹ 25 more rowsloughran_counts %>%
group_by(sentiment) %>%
slice_max(n, n = 10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(n, word, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(x = "Contribution to sentiment",
y = NULL)
tidy_books_tales %>%
filter(book == "The tinder-box") %>%
filter(language == "English") %>%
anti_join(stop_words) %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))## Joining with `by = join_by(word)`
tidy_books_tales %>%
filter(book == "The tinder-box") %>%
inner_join(get_sentiments("bing")) %>%
filter(language == "English") %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = c("gray20", "gray80"),
max.words = 100)## Joining with `by = join_by(word)`
afinn_txt = Tinder %>%
inner_join(get_sentiments("afinn")) %>%
summarise(sentiment = sum(value)) %>%
mutate(method = "AFINN")## Joining with `by = join_by(word)`bing_and_nrc_txt = bind_rows(
Tinder %>%
inner_join(get_sentiments("bing")) %>%
mutate(method = "Bing et al."),
Tinder %>%
inner_join(get_sentiments("nrc") %>%
filter(sentiment %in% c("positive",
"negative"))
) %>%
mutate(method = "NRC")) %>%
count(method, sentiment) %>%
pivot_wider(names_from = sentiment,
values_from = n,
values_fill = 0) %>%
mutate(sentiment = positive - negative)## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`## Warning in inner_join(., get_sentiments("nrc") %>% filter(sentiment %in% : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1050 of `x` matches multiple rows in `y`.
## ℹ Row 4705 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.bind_rows(afinn,
bing_and_nrc) %>%
ggplot(aes(index, sentiment, fill = method)) +
geom_col(show.legend = FALSE) +
facet_wrap(~method, ncol = 1, scales = "free_y")
CONCLUSION
When I first counted the sentiments, under AFINN, positive and negative sentiments were equal in values with uncertainty coming in in second place. Thus I expected an equal amount of negative and positive words popping up at the same frequency in the visuals. Side note when we did tally up the frequency of each word, the highest was could which was categorize as uncertainty and then out of the Top 10 words to frequently appear most were under the category positive. Thus I changed my theory that the visual would lean towards positive sentiment analysis.
However when created the visuals to track sentiment along the storyline in all three lexicons, the story The-tinder box is predominately assessed as positive. ## R Markdown
This is an R Markdown document
SOURCE Robinson, J. S. and D. (n.d.). 2 sentiment analysis with Tidy Data: Text mining with R. 2 Sentiment analysis with tidy data | Text Mining with R. 11/03/23.< https://www.tidytextmining.com/sentiment.html>
EmilHvitfeldt. (n.d.). Emilhvitfeldt/hcandersenr: An R package for H.C. Andersens Fairy tales. GitHub.11/04/2023.https://cran.r-project.org/web/packages/hcandersenr/hcandersenr.pdf https://github.com/EmilHvitfeldt/hcandersenr
https://cran.r-project.org/web/packages/hcandersenr/hcandersenr.pdf https://github.com/EmilHvitfeldt/hcandersenr https://rdrr.io/cran/hcandersenr/f/README.md
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