This report is an exploration of the artist Chance the Rapper.

I will be looking at how/if Chance’s music becomes more positive as his life becomes positive. I will be looking at his four main albums “10 Day” (2012), “Acid Rap” (2013), “Coloring Book” (2016), and “The Big Day” (2019). I chose only to look at these works with the help of Wikipedia and Spotify. Although Spotify only has the three albums “Acid Rap”, “Coloring Book”, and “The Big Day” on it’s platform, if you go to the about section under the artist, Spotify recognizes “10 Day” as Chance the Rapper’s first real mixtape/ album (mixtape and album are used interchangeably in his case). With that said, Spotify recognizes his second album “Acid Rap” as his first to hit the charts. Using “10 Day” in my analysis can be useful to show how his music changes as his fame grows. I will be using the Genuis API to get my necessary information

First I download the necessary packages

library(genius)
library(tidyverse)
library(tidytext)
library(textdata)
library(wordcloud2)
library(ggplot2)

Here I am downloading each individual album

“10 Day”

10 Day is Chance the Rapper’s real first mixtape. This mixtape was released during a very bad period in his life. Chance was addicted to drugs and his music career was having a hard time taking off.

tenday <- genius_album(artist = "Chance the Rapper", album = "10 Day")
View(tenday)

How many words are in this album

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1  7043

7043

How many times do the most popular words occur within the album

tenday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  knitr::kable()
word n
juke 108
fuck 97
bout 68
nigga 62
tahm 61
mama 37
time 36
break 32
niggas 32
shit 32

The most used words are fairly negative

What words stand out the most in a word cloud

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  wordcloud2()

“Juke”, “Fuck” “Bout”

“Acid Rap”

During Acid Rap, Chance is still going through his drug addiction but his music career is starting to take off. The artist “Tyler the Creator” allowed Chance to open for him allowing his music to reach new audiences. In the song “Cocoa Butter Kisses” you can see his family’s disapproval of his addiction in the lyrics: “Put Visine inside my eyes so my grandma would fucking hug me.”

acidrap <- genius_album(artist = "Chance the Rapper", album = "Acid Rap")
View(acidrap)

How many words are in this album

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1  7781

7781

How many times do the most popular words occur within the album

acidrap %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  knitr::kable()
word n
na 119
love 58
juice 52
shit 51
jam 49
yeah 49
kisses 39
igh 34
ooh 33
smoke 33

The top three words used are fairly positive in this album (maybe a sign of growth?)

What words stand out the most in a word cloud

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("na")) %>% 
  wordcloud2()

“love”, “jam”, “yeah”, “juice”, “shit”, “kisses”, “smoke”, “fuck”

Coloring Book

Coloring Book is the first album where Chance’s music has more of a gospel-y sound. His music begins to focus on moving forward from his past and focusing on his future and his music. In the song “Finish Line/ Drown” he states: “Last year got addicted to Xans, Started forgetting my name and started missing my chance” showing how his mistakes were holding him back. Chance also recognizes the good things in his life. In the song “Blessings” he says: “I know the difference in blessings and worldly possessions, Like my ex-girl getting pregnant, And her becoming my everything”

coloringbook <- genius_album(artist = "Chance the Rapper", album = "Coloring Book")
View(coloringbook)

How many words are in this album

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1  7665

7665

How many times do the most popular words occur within the album

coloringbook %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  knitr::kable()
word n
ready 44
blessings 35
god 33
night 32
ayy 28
drinking 28
bom 27
citywide 27
gon 27
drugs 24

The top 5 most used words in the song are very positive

What words stand out the most in a word cloud

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  wordcloud2()

“ready”, “blessings”, “god”, “night”, “drinking”, “deserve”, “friends”, “citywide”

The Big Day

The Big Day is Chance the Rapper’s most recent and most mainstream album. He begins to move away from his Gospel/Blues-y vibe and into a more pop like tone.

bigday <- genius_album(artist = "Chance the Rapper", album = "The Big Day")
View(bigday)

How many words are in this album

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1 11722

11722

How many times do the most popular words occur within the album

bigday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  knitr::kable()
word n
yeah 141
ooh 66
single 66
love 54
time 45
baby 41
bag 38
forever 37
die 36
step 33

The top 8 most used words in this song are very positive

What words stand out the most in a word cloud

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("yeah", "ooh")) %>% 
  wordcloud2()

“single”, “love”, “baby”, “time”, “forever”

Downloading all albums together for a joint analysis

chance_albums <- tribble(
  ~artist, ~title,
  "Chance the Rapper", "10 Day",
  "Chance the Rapper", "Acid Rap",
  "Chance the Rapper", "Coloring Book",
  "Chance the Rapper", "The Big Day"
)
View(chance_albums)
chance_lyrics <- chance_albums %>% 
  add_genius(artist, title, type="album")

All lyrics from all albums

View(chance_lyrics)

Removing any stop and soft words leaving only sentiment words left

chance_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) -> chance_word_count

What words occur the most throughout all his albums

wordcloud2(chance_word_count)

“yeah”, “fuck”, “juke”, “love”, “time”, “nigga”, “shit”, “god”

How much the 20 most common words are used throughout the albums

chance_word_count %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

Finding the sentiments of the most common used words throughout the albums

chance_word_count %>% 
  inner_join(get_sentiments("afinn"))-> chance_sentiment
View(chance_sentiment)

What are the most negative sentiment words

chance_sentiment %>% 
  arrange(desc(-value))
## # A tibble: 351 x 3
##    word              n value
##    <chr>         <int> <dbl>
##  1 niggas           78    -5
##  2 bitch            58    -5
##  3 bitches          10    -5
##  4 motherfucking     3    -5
##  5 cock              1    -5
##  6 motherfucker      1    -5
##  7 prick             1    -5
##  8 fuck            160    -4
##  9 shit            110    -4
## 10 ass              33    -4
## # … with 341 more rows

The n-word is probably not used in a negative way rather talking about people he probably considers family. An example of this is in the song “No Problem”: “If one more label try to stop me, It’s gon’ be some dreadhead niggas in ya lobby, huh huh”. In this song the “dreadhead niggas” he is refering to are the other artists from Chicago he wants to collaborate with, but is not allowed to due to record labels trying to control him and his music https://genius.com/9140989.

What is the overall sentiment of his works

mean(chance_sentiment$value)
## [1] -0.4501425

-0.4501425

Are the words used in Chance’s lyrics positive or negative?

chance_word_count %>% 
  inner_join(get_sentiments("bing")) -> chance_sentiment2
View(chance_sentiment2)

How many negative words are there in comparison to positive words?

chance_sentiment2 %>% 
  ggplot() + geom_col(aes(sentiment, n)) + theme_bw()

There are many more negative words overall than positive throughout his works

I will now look at the explicit words within Chance the Rapper’s albums

I will be looking at if the number of curse words go down throughout each album. The reduction in curse words throughout his music as each album passes represents his changing image. Within 10 Day Chance is a drug addict with a failing music career. In Acid Rap Chance’s music is finally taking off and hitting the charts. In Coloring Book Chance is over his addiction and back in love with his pregnant ex girlfriend. In The Big Day Chance is at the height of his music fame and a father.

Most frequent explicit words with highest negative sentiment throughout all albums

chance_word_count %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(value == "-5"|value == "-4") %>% 
  arrange(desc(n))
## # A tibble: 20 x 3
##    word              n value
##    <chr>         <int> <dbl>
##  1 fuck            160    -4
##  2 shit            110    -4
##  3 niggas           78    -5
##  4 bitch            58    -5
##  5 ass              33    -4
##  6 fucking          28    -4
##  7 damn             27    -4
##  8 fucked           14    -4
##  9 dick             12    -4
## 10 bitches          10    -5
## 11 hell              4    -4
## 12 bullshit          3    -4
## 13 motherfucking     3    -5
## 14 asshole           2    -4
## 15 pissed            2    -4
## 16 cock              1    -5
## 17 fucker            1    -4
## 18 motherfucker      1    -5
## 19 prick             1    -5
## 20 torture           1    -4

10 Day without any stop or soft words

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> tendayexplicit

How often are the 10 most explicit words throughout all albums used in 10 Day?

tendayexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

“fuck” is used nearly 100 times within the album

Acid Rap without any stop or soft words

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> acidrapexplicit

How often are the 10 most explicit words throughout all albums used in Acid Rap?

acidrapexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

Although “shit” is used almost 50 times throughout the album, “fuck” has reduced to only about 30 times. The amount of overall curse words has decreased.

Coloring Book without any stop or soft words

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> coloringbookexplicit

How often are the 10 most explicit words throughout all albums used in Acid Rap?

coloringbookexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

“fuck” and “bitch” are only used about 15 times throughout this album, a big drop in comparison to the others. Most gospel like album, could be why there is such a decrease in curse words.

The Big Day without any stop or soft words

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> bigdayexplicit

How often are the 10 most explicit words throughout all albums used in The Big Day?

bigdayexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

“fuck” goes back up to being used around 20 times. While there is still a decrease in overall curse words in this album, this increase could have to do with the more “mainstream” sound he is trying to achieve in The Big Day.

Explicit Conclusion:

All main 10 curse words throughout all Chance’s albums decrease in each album. I believe the large explicitness in 10 Day is Chance releasing his anger towards the world. Using so many curse words is representative of his failures. That could be a big reason why this first album did not take off. In Acid Rap the amount of curse words decrease by almost half. This could relate to his music career taking off. There is a new lightness to his music even though he is still experiencing drug addiction. In Coloring Book the explicitness decreases by another third from the amount in Acid Rap. Coloring Book is Chance at the happiest we have seen him. He is over his drug addiction and reflecting on his past. He uses his music to continue forward and look at the blessings around him “Like {his}-girl getting pregnant, And her becoming {his} everything”. The Big Day is released three years after Coloring Book. During this time Chance’s fame is growing and he become a father. This time allows for a lot of growth but also possibly a new image/sound. The release of The Big Day looks into a new tone in Chance’s music. The Big Day is extremely pop-y and mainstream sounding. I believe the slight increase in explicitness in The Big Day from Coloring Book is an example of the more mainstream look he is going for.

Overall Conclusion:

From my analysis I believe Chance the Rapper’s music did become more positive as his life became more positive. Looking at the most used words within each album I can see the words he chooses to repeat the most become more positive words album after album. Looking at the frequency of curse words within each album also shows the decline in negativity or even need for those words as time continues. I have proved my hypothesis and I look forward to continuing this analysis for the Final Project using the Spotify API.