Cultural Representation
Julia Parker
5/5/2021
Cultural representation is seen in all cultures and backgrounds. It is what makes a certain population or group distinct and different. Looking into America specifically cultural representation has revolutionized over the centuries. Especially in art and media. Music and poems for example are cultural ways of expression. They can mention the time period and the good and bad events playing out. Culture and art bring people together and is a great way to relate to someone and share a connection.
Poe’s poem “The Tell-Tale Heart” is an example of a piece of literature about an insane individual that is a killer. Because it is in first person the reader can be put into the life of the narrator maybe even sympathize with him. The reader can pick up on all the emotions the killer is feeling. The poem mentions a murder and the process of how the murderer was caught (Giordano, n.d.). The narrator was hearing voices and the heartbeat of the victim he killed (Giordano, n.d.). This poem has some similarities to the song “The Monster” we listened to in class. Eminem talked about the voices he was hearing and the inner demons he was facing. The narrator too could not escape the voices inside his head even if they weren’t there.
“The Yellow Wallpaper” was a piece of writing that described a woman being put in a room to stare at essentially yellow wallpaper because her husband thought she was suffering from a mental illness (Perkins, n.d.). She had many things taken away from her, her child and writing for example. There was a sense of manipulation from the husband in this story, one example being, that he would not let her journal or write her feelings while being in the room. It was easier to sympathize with the women rather than the man in “The Tell-Tale Heart” because it seems she did nothing wrong. She was put in that room against her will. Even today there is literature about women being under the authority of men. When discussing songs that incorporate mental illness I thought of “Mad Woman” by Taylor Swift right away. She uses this song as an empowering message towards women who have been called crazy. Taylor can relate to the feeling of men and critics calling her crazy, over-dramatic, overemotional, and more. Eventually when someone calls you crazy enough times it’s no surprise you become crazy. She uses the term that she has become a mad woman as a metaphor.
Music is a very special form of cultural representation. Hidden meanings and clues can be demonstrated and played around with. A song may mention someone being crazy, for example the song by Patsy Cline. But is she crazy or just in love and that love is making her crazy and vulnerable to feelings? It is a metaphor for her feelings. The other song we listened to in class was “I’ve put a spell on you”. It was performed by Screaming Jay Hawkins who played into the stereotypes of an African American man. There was an animalistic factor to his performance that he took advantage of. He had tusks and his body language was a little threatening. Often, he yelled and was speaking in words that were hard to understand. Both Pasty and Screaming Jay Hawkins portrayed someone “crazy”, but the execution was different.
Looking at the data graphs, words such as joy, trust, fear, anger, disgust, and sadness often appeared in poems in the Victorian and Contemporary Era. All those words relate to emotion. The graphs helped visualize the words being used more in a certain time period than the other. The Victorian Era mentioned the emotion filled words more whether positive or negative. Which I am a little surprised about because mental illness and feelings were tabooer in that era than the Contemporary Era. But along with those negative words in the Victorian Era there were positive words. Those positive words were used more, which I think was because emotions were sugarcoated. For example, the words trust, and joy were used more than anger and disgust.
Furthermore, this unit was the shortest but was my favorite. The poems and music we listened to really helped me understand the culture behind mental illness and how it is still seen today. I see many similarities in women specifically dealing with mental health. There was a theme of sexism throughout all the units. The poems and music discussed were easy to understand and were relatable.
Sources:
Giordano, R. (n.d.). The Tell-Tale Heart by Edgar Allan Poe. PoeStories.com. https://poestories.com/read/telltaleheart.
Perkins Stetson, C. (n.d.). THE YELLOW WALLPAPER. https://www.nlm.nih.gov/exhibition/theliteratureofprescription/exhibitionAssets/digitalDocs/The-Yellow-Wall-Paper.pdf.
Import Data
library(readtext)## Warning: package 'readtext' was built under R version 4.0.5library(dplyr)## Warning: package 'dplyr' was built under R version 4.0.4##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionpoems_hunter <- readtext("~//Madness/poems_hunter") %>% tibble()
poems_hunter## # A tibble: 10 x 2
## doc_id text
## <chr> <chr>
## 1 1_Hunter_2009.t~ "It's A Mad(Off) , Mad(Off) , Mad(Off) , Mad(Off) World\nWh~
## 2 10_Hunter_2008.~ "Mad Mad World\n\nthe world is mad\ninsane!\nchristians fig~
## 3 2_Hunter_2014.t~ "What Is This Mad Race Of The Mad-Mad Modern Man?\n\nWhat i~
## 4 3_Hunter_2009.t~ "It Is A Mad, Mad, Mad World.........\n\nMoon is gone\nbut ~
## 5 4_Hunter_2014.t~ "Poetry, Poetry, Poetry, Will Madden Me And You/ You Poetry~
## 6 5_Hunter_2014.t~ "The Poets Are The Mad Men And Poetry A Mad Man's Babbling\~
## 7 6_Hunter_2013.t~ "A Mad Mad Mothers Mistake\n\nLeft left not right she's lef~
## 8 7_Hunter_2010.t~ "Thoughts In Madness, Madness In Thought\n\nMy days are mad~
## 9 8_Hunter_2012.t~ "Traces Of Madness (Madman's Song)\n\nYou would have said, ~
## 10 9_Hunter_2010.t~ "If Mad Is A Hatter Then Mad Am I\n\nIf mad is a hatter the~poems_victorian <- readtext("~//Madness/poems_victorian") %>% tibble()
poems_victorian## # A tibble: 10 x 2
## doc_id text
## <chr> <chr>
## 1 1_Victorian_1850.~ "THE BALLAD OF RICHARD BURNELL.\n\nFrom his bed rose Rich~
## 2 10_Victorian_1820~ "The following touching Verses are taken from a Newcastle~
## 3 2_Victorian_1820.~ "THE BRANCHERS.*\n\n1.\nI sat to bask, one sunny morn,\n1~
## 4 3_Victorian_1890.~ "THE BALLAD OF THE KING’S JEST.\n\nWhen springtime flus~
## 5 4_Victorian_1850.~ "THE PENITENT FREE-TRADER.\n\nTufnell ! For the love of~
## 6 5_Victorian_1820.~ "STANZAS.\n\n“ —— And muttered, lost ! lost ! lost ~
## 7 6_Victorian_1860.~ "XV.—THE MOTHER’S LAMENT.\n\nWhen I was young, when I~
## 8 7_Victorian_1880.~ "A Stray Sunbeam.\n\nA\nSUNBEAM gone astray\n1\nUpon life~
## 9 8_Victorian_1870.~ "LADY NOEL BYRON.\n\nA\nND as she spoke, it seemed as tho~
## 10 9_Victorian_1840.~ "The Auld State Kirk.\nNEW SONG.\nTune—“ Auld Lang Sy~Join datasets
poems_raw <- rbind(poems_hunter, poems_victorian)
poems_raw## # A tibble: 20 x 2
## doc_id text
## <chr> <chr>
## 1 1_Hunter_2009.txt "It's A Mad(Off) , Mad(Off) , Mad(Off) , Mad(Off) World\n~
## 2 10_Hunter_2008.txt "Mad Mad World\n\nthe world is mad\ninsane!\nchristians f~
## 3 2_Hunter_2014.txt "What Is This Mad Race Of The Mad-Mad Modern Man?\n\nWhat~
## 4 3_Hunter_2009.txt "It Is A Mad, Mad, Mad World.........\n\nMoon is gone\nbu~
## 5 4_Hunter_2014.txt "Poetry, Poetry, Poetry, Will Madden Me And You/ You Poet~
## 6 5_Hunter_2014.txt "The Poets Are The Mad Men And Poetry A Mad Man's Babblin~
## 7 6_Hunter_2013.txt "A Mad Mad Mothers Mistake\n\nLeft left not right she's l~
## 8 7_Hunter_2010.txt "Thoughts In Madness, Madness In Thought\n\nMy days are m~
## 9 8_Hunter_2012.txt "Traces Of Madness (Madman's Song)\n\nYou would have said~
## 10 9_Hunter_2010.txt "If Mad Is A Hatter Then Mad Am I\n\nIf mad is a hatter t~
## 11 1_Victorian_1850.~ "THE BALLAD OF RICHARD BURNELL.\n\nFrom his bed rose Rich~
## 12 10_Victorian_1820~ "The following touching Verses are taken from a Newcastle~
## 13 2_Victorian_1820.~ "THE BRANCHERS.*\n\n1.\nI sat to bask, one sunny morn,\n1~
## 14 3_Victorian_1890.~ "THE BALLAD OF THE KING’S JEST.\n\nWhen springtime flus~
## 15 4_Victorian_1850.~ "THE PENITENT FREE-TRADER.\n\nTufnell ! For the love of~
## 16 5_Victorian_1820.~ "STANZAS.\n\n“ —— And muttered, lost ! lost ! lost ~
## 17 6_Victorian_1860.~ "XV.—THE MOTHER’S LAMENT.\n\nWhen I was young, when I~
## 18 7_Victorian_1880.~ "A Stray Sunbeam.\n\nA\nSUNBEAM gone astray\n1\nUpon life~
## 19 8_Victorian_1870.~ "LADY NOEL BYRON.\n\nA\nND as she spoke, it seemed as tho~
## 20 9_Victorian_1840.~ "The Auld State Kirk.\nNEW SONG.\nTune—“ Auld Lang Sy~Clean data
library(tidyr)## Warning: package 'tidyr' was built under R version 4.0.5poems <- poems_raw %>% separate(doc_id, c("ID","Database","Year"))## Warning: Expected 3 pieces. Additional pieces discarded in 20 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].poems## # A tibble: 20 x 4
## ID Database Year text
## <chr> <chr> <chr> <chr>
## 1 1 Hunter 2009 "It's A Mad(Off) , Mad(Off) , Mad(Off) , Mad(Off) Worl~
## 2 10 Hunter 2008 "Mad Mad World\n\nthe world is mad\ninsane!\nchristian~
## 3 2 Hunter 2014 "What Is This Mad Race Of The Mad-Mad Modern Man?\n\nW~
## 4 3 Hunter 2009 "It Is A Mad, Mad, Mad World.........\n\nMoon is gone\~
## 5 4 Hunter 2014 "Poetry, Poetry, Poetry, Will Madden Me And You/ You P~
## 6 5 Hunter 2014 "The Poets Are The Mad Men And Poetry A Mad Man's Babb~
## 7 6 Hunter 2013 "A Mad Mad Mothers Mistake\n\nLeft left not right she'~
## 8 7 Hunter 2010 "Thoughts In Madness, Madness In Thought\n\nMy days ar~
## 9 8 Hunter 2012 "Traces Of Madness (Madman's Song)\n\nYou would have s~
## 10 9 Hunter 2010 "If Mad Is A Hatter Then Mad Am I\n\nIf mad is a hatte~
## 11 1 Victorian 1850 "THE BALLAD OF RICHARD BURNELL.\n\nFrom his bed rose R~
## 12 10 Victorian 1820 "The following touching Verses are taken from a Newcas~
## 13 2 Victorian 1820 "THE BRANCHERS.*\n\n1.\nI sat to bask, one sunny morn,~
## 14 3 Victorian 1890 "THE BALLAD OF THE KING’S JEST.\n\nWhen springtime f~
## 15 4 Victorian 1850 "THE PENITENT FREE-TRADER.\n\nTufnell ! For the love~
## 16 5 Victorian 1820 "STANZAS.\n\n“ —— And muttered, lost ! lost ! lo~
## 17 6 Victorian 1860 "XV.—THE MOTHER’S LAMENT.\n\nWhen I was young, whe~
## 18 7 Victorian 1880 "A Stray Sunbeam.\n\nA\nSUNBEAM gone astray\n1\nUpon l~
## 19 8 Victorian 1870 "LADY NOEL BYRON.\n\nA\nND as she spoke, it seemed as ~
## 20 9 Victorian 1840 "The Auld State Kirk.\nNEW SONG.\nTune—“ Auld Lang~Tokenize text data
library(tidytext)## Warning: package 'tidytext' was built under R version 4.0.4library(stringr)## Warning: package 'stringr' was built under R version 4.0.4poems_cleaned <- poems %>%
unnest_tokens(output = word, input = text) %>%
anti_join(stop_words) %>%
filter(!str_detect(word, "[^a-zA-Z\\s]|mad")) %>%
mutate(Database = str_replace(Database, "Hunter", "Contemporary"))## Joining, by = "word"poems_cleaned## # A tibble: 3,552 x 4
## ID Database Year word
## <chr> <chr> <chr> <chr>
## 1 1 Contemporary 2009 world
## 2 1 Contemporary 2009 bernard
## 3 1 Contemporary 2009 investors
## 4 1 Contemporary 2009 banker
## 5 1 Contemporary 2009 globe
## 6 1 Contemporary 2009 money
## 7 1 Contemporary 2009 adolph
## 8 1 Contemporary 2009 hitler
## 9 1 Contemporary 2009 blamed
## 10 1 Contemporary 2009 jewish
## # ... with 3,542 more rowsVisualize most frequent words
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5poems_cleaned %>%
count(Database, word, sort = TRUE) %>%
group_by(Database) %>%
top_n(10, n) %>%
ungroup() %>%
ggplot(aes(x = n, y = reorder_within(word, n, Database),
fill = Database)) +
geom_col(alpha = .8) +
facet_wrap(~Database, scales = "free_y") +
scale_y_reordered() +
labs(y = NULL,
x = "Word Frequency",
title = "Top 10 Most Frequent Words")
Sentiment Analysis
Using NRC Lexicon
nrc <- get_sentiments ("nrc")
nrc## # A tibble: 13,901 x 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
## # ... with 13,891 more rowspoems_cleaned %>%
inner_join(nrc) %>%
count(Database, sentiment, sort = TRUE) %>%
ggplot(aes(y = reorder_within(sentiment, n, Database), x = n, fill = Database)) +
geom_col(alpha = 0.8) +
facet_wrap(~Database, scales = "free_y") +
scale_y_reordered() +
labs(title = "Number of Words Associated with Emotions",
y = "Emotions",
x = "Number of Words")## Joining, by = "word"
Using bing Lexicon
bing <- get_sentiments("bing")
bing## # A tibble: 6,786 x 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
## # ... with 6,776 more rowspoems_cleaned %>%
inner_join(bing) %>%
ggplot(aes(x = Database, fill = sentiment)) +
geom_bar(position = "fill") +
labs(title = "Ratios of Negative and Positive Words",
y = "Proportions",
x = NULL)## Joining, by = "word"
Using AFINN Lexicon
affin <- get_sentiments("afinn")
affin## # A tibble: 2,477 x 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
## # ... with 2,467 more rowspoems_cleaned %>%
inner_join(affin) %>%
group_by(Database) %>%
summarise(sentiment_score = sum(value)) %>%
ungroup() %>%
ggplot(aes(x = Database, y = sentiment_score)) +
geom_col(fill = "midnightblue", alpha = 1)## Joining, by = "word"
labs(title = "Sum of sentiment Scores of Words",
x = NULL,
y = "Sum of Sentiment Scores")## $x
## NULL
##
## $y
## [1] "Sum of Sentiment Scores"
##
## $title
## [1] "Sum of sentiment Scores of Words"
##
## attr(,"class")
## [1] "labels"