Chance the Rapper has had his music career followed criticially since his debut mix tape, “10 Day”, from 2012. He has followed this up with the albums Acid Rap, Coloring Book, and The Big Day. In recent years, fans of his have noted a significant change in quality and tone of his music over the years. This text analysis of Chance the Rapper’s four biggest works will try to calculate if these claims have any merit. This will look at the overall sentiment of each album, along with comparing the most frequently used words in comparison to the other pieces of work.
This will begin with installing all of the required files, scraping the lyrics and assigning those lyrics to groups that can be analyzed.
#install.packages("rjson")
#install.packages("json")
library(jsonlite)
library(tidyverse)
#install.packages('tidytext')
library(tidytext)
library(textdata)
thebigday <- fromJSON("~/desktop/lyrics_TheBigDay.json")
coloringbook <- fromJSON("~/desktop/lyrics_ColoringBook.json")
tenday <- fromJSON("~/desktop/lyrics_10Day.json")
acidrap <- fromJSON("~/desktop/lyrics_AcidRap.json")The first step was to look at the most common words of each album and see how many times those words were used. The first album looked at was The Big Day.
as.data.frame(thebigday$tracks$song) -> thebigday_df
thebigday_words <- thebigday_df %>%
unnest_tokens(word, lyrics)
thebigday_words %>% count(word, sort = TRUE) %>%
anti_join(stop_words) %>%
head(25) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3"))## word n
## 1 yeah 138
## 2 chance 83
## 3 rapper 79
## 4 single 67
## 5 ooh 66
## 6 love 54
## 7 time 46
## 8 baby 41
## 9 bag 39
## 10 forever 37
## 11 die 36
## 12 day 34
## 13 step 33
## 14 slide 30
## 15 uh 30
## 16 people 29
## 17 god 27
## 18 yuh 24
## 19 wanna 23
## 20 ayy 21
## 21 gon 21
## 22 gotta 21The next album looked at and its most common words was Coloring Book.
as.data.frame(coloringbook$tracks$song) -> coloringbook_df
coloringbook_words <- coloringbook_df %>%
unnest_tokens(word, lyrics)
coloringbook_words %>% count(word, sort = TRUE) %>%
anti_join(stop_words) %>%
head(25) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3"))## word n
## 1 chance 64
## 2 rapper 64
## 3 ready 44
## 4 night 36
## 5 blessings 34
## 6 god 33
## 7 ayy 28
## 8 drinking 28
## 9 bom 27
## 10 gon 27
## 11 deserve 25
## 12 drugs 25
## 13 citywide 24
## 14 na 24
## 15 friends 23
## 16 79th 22
## 17 huh 21
## 18 hey 20
## 19 juke 18
## 20 time 17
## 21 wanna 17
## 22 praise 16Many standout words were more positive, such as “blessings”, “ready”, and “god”. The next album looked at was 10 Day.
as.data.frame(tenday$tracks$song) -> tenday_df
tenday_words <- tenday_df %>%
unnest_tokens(word, lyrics)
tenday_words %>% count(word, sort = TRUE) %>%
anti_join(stop_words) %>%
head(25) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3"))## word n
## 1 juke 110
## 2 fuck 98
## 3 bout 69
## 4 nigga 62
## 5 tahm 62
## 6 time 38
## 7 mama 37
## 8 break 32
## 9 niggas 32
## 10 shit 32
## 11 brain 27
## 12 cells 27
## 13 burned 24
## 14 chance 24
## 15 day 22
## 16 gon 19
## 17 chi 18
## 18 fucking 18
## 19 rapper 18
## 20 fam 17
## 21 love 17
## 22 til 17
## 23 town 17Swear words covered the most common words in this album. There was a strong difference in the type of language used from the Coloring Book and 10 Day. The last album is Acid Rap.
as.data.frame(acidrap$tracks$song) -> acidrap_df
acidrap_words <- acidrap_df %>%
unnest_tokens(word, lyrics)
acidrap_words %>% count(word, sort = TRUE) %>%
anti_join(stop_words) %>%
head(25) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3"))## word n
## 1 na 119
## 2 chance 59
## 3 love 59
## 4 rapper 58
## 5 juice 53
## 6 shit 51
## 7 yeah 49
## 8 jam 48
## 9 kisses 40
## 10 smoke 34
## 11 ooh 33
## 12 igh 31
## 13 fuck 29
## 14 bout 27
## 15 bitch 24
## 16 nigga 21
## 17 miss 20
## 18 niggas 20
## 19 cigarettes 19
## 20 talk 19
## 21 time 19
## 22 butter 18
## 23 cocoa 18Acid Rap had a variety of positive and negative words in the most common list.
The next step to better visualize the words used in these albums was to create a word cloud of the most common words. This began with downloading the required packages to make a word cloud.
#install.packages("wordcloud")
library(wordcloud2)
library(devtools)
#install.packages("devtools")
#install_github('lchiffon/wordcloud2')The first album to look at in the cloud is The Big Day.
thebigday_words %>%
anti_join(stop_words) %>%
count(word, sort = TRUE) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3")) %>%
head(50) %>%
wordcloud2()The next album is Coloring Book.
coloringbook_words %>%
anti_join(stop_words) %>%
count(word, sort = TRUE) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3")) %>%
head(50) %>%
wordcloud2()Next up is 10 Day.
tenday_words %>%
anti_join(stop_words) %>%
count(word, sort = TRUE) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3")) %>%
head(50) %>%
wordcloud2()The last word cloud is for Acid Rap.
acidrap_words %>%
anti_join(stop_words) %>%
count(word, sort = TRUE) %>%
filter(!word %in% c("verse", "1", "chorus", "2", "3")) %>%
head(50) %>%
wordcloud2()To properly determine if the albums were overall more positive or negative with their language usage, this code was used to rank every word on a -5 to 5 scale with the more hateful words being closer to a -5 and the happier words being closer to 5 with neutral words falling closer to 0. An overall count was factored in to rank how many positive and negative words were included in the albums.
Here is the sentiment analysis for The Big Day.
thebigday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(-value)) %>%
head(25)## word value n
## 1 niggas -5 17
## 2 bitch -5 10
## 3 bitches -5 2
## 4 cock -5 1
## 5 fuck -4 19
## 6 shit -4 16
## 7 damn -4 11
## 8 ass -4 6
## 9 bullshit -4 2
## 10 dick -4 1
## 11 fucked -4 1
## 12 fucking -4 1
## 13 pissed -4 1
## 14 die -3 36
## 15 bad -3 14
## 16 dead -3 2
## 17 fake -3 2
## 18 cheat -3 1
## 19 cheats -3 1
## 20 cruel -3 1
## 21 damage -3 1
## 22 died -3 1
## 23 faking -3 1
## 24 goddamn -3 1
## 25 hate -3 1thebigday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(value)) %>%
head(25)## word value n
## 1 win 4 1
## 2 love 3 54
## 3 super 3 5
## 4 glad 3 4
## 5 excite 3 2
## 6 fan 3 2
## 7 grateful 3 2
## 8 lovely 3 2
## 9 perfect 3 2
## 10 celebrate 3 1
## 11 goodness 3 1
## 12 grand 3 1
## 13 lucky 3 1
## 14 popular 3 1
## 15 woo 3 1
## 16 chance 2 83
## 17 worth 2 15
## 18 hug 2 8
## 19 sweet 2 7
## 20 top 2 7
## 21 fine 2 4
## 22 kiss 2 4
## 23 proud 2 4
## 24 true 2 4
## 25 clean 2 3thebigday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value < 0) %>%
count()## n
## 1 317thebigday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value > 0) %>%
count()## n
## 1 470It is important to note that in this album, the word love was used significantly more than any of the overwhelmingly negative swear words. Overall, there were 317 repeated negative words in this album, but that was shadowed by the 470 positive words.
Here is the same code ran on Coloring Book.
coloringbook_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(-value)) %>%
head(25)## word value n
## 1 bitch -5 15
## 2 niggas -5 9
## 3 bitches -5 4
## 4 fuck -4 14
## 5 shit -4 11
## 6 damn -4 6
## 7 hell -4 3
## 8 fucked -4 2
## 9 ass -4 1
## 10 dick -4 1
## 11 bad -3 9
## 12 lost -3 5
## 13 die -3 2
## 14 kill -3 2
## 15 worried -3 2
## 16 cheat -3 1
## 17 crime -3 1
## 18 died -3 1
## 19 faking -3 1
## 20 mad -3 1
## 21 worry -3 1
## 22 drown -2 6
## 23 swear -2 5
## 24 crazy -2 4
## 25 lobby -2 3coloringbook_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(value)) %>%
head(25)## word value n
## 1 miracle 4 11
## 2 win 4 1
## 3 praise 3 16
## 4 love 3 12
## 5 praises 3 10
## 6 haha 3 9
## 7 woo 3 8
## 8 happy 3 2
## 9 perfect 3 2
## 10 blockbuster 3 1
## 11 charm 3 1
## 12 fan 3 1
## 13 grand 3 1
## 14 loyalty 3 1
## 15 lucky 3 1
## 16 nice 3 1
## 17 successful 3 1
## 18 super 3 1
## 19 chance 2 64
## 20 care 2 11
## 21 true 2 5
## 22 ha 2 4
## 23 freedom 2 3
## 24 smile 2 3
## 25 worthy 2 3coloringbook_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value < 0) %>%
count()## n
## 1 181coloringbook_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value > 0) %>%
count()## n
## 1 297Coloring Book follows the same trend as The Big Day where there are significantly more positive words used than negative with that being 297 to 181. This would meet the expectations of fans that assumed these two albums had much more positive themes than 10 Day and Acid Rap.
tenday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(-value)) %>%
head(25)## word value n
## 1 niggas -5 32
## 2 bitch -5 9
## 3 bitches -5 1
## 4 prick -5 1
## 5 fuck -4 98
## 6 shit -4 32
## 7 fucking -4 18
## 8 ass -4 14
## 9 fucked -4 11
## 10 dick -4 3
## 11 damn -4 2
## 12 asshole -4 1
## 13 torture -4 1
## 14 worried -3 8
## 15 bad -3 7
## 16 lost -3 3
## 17 fake -3 2
## 18 mad -3 2
## 19 arrested -3 1
## 20 awful -3 1
## 21 dead -3 1
## 22 die -3 1
## 23 dumb -3 1
## 24 haters -3 1
## 25 idiot -3 1tenday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(value)) %>%
head(25)## word value n
## 1 funny 4 9
## 2 fun 4 3
## 3 love 3 17
## 4 nice 3 3
## 5 luck 3 2
## 6 classy 3 1
## 7 fan 3 1
## 8 glad 3 1
## 9 haha 3 1
## 10 loved 3 1
## 11 lovely 3 1
## 12 loyalty 3 1
## 13 chance 2 24
## 14 smile 2 10
## 15 hope 2 9
## 16 hoping 2 7
## 17 saved 2 7
## 18 save 2 6
## 19 favors 2 4
## 20 true 2 3
## 21 fair 2 2
## 22 smiles 2 2
## 23 accomplished 2 1
## 24 comfortable 2 1
## 25 enjoying 2 1tenday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value < 0) %>%
count()## n
## 1 338tenday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value > 0) %>%
count()## n
## 1 18410 Day flips back the complete opposite way. There are 338 words with negative sentiment with only 184 positive words used.
acidrap_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(-value)) %>%
head(25)## word value n
## 1 bitch -5 24
## 2 niggas -5 20
## 3 bitches -5 3
## 4 motherfucking -5 3
## 5 motherfucker -5 1
## 6 shit -4 51
## 7 fuck -4 29
## 8 ass -4 14
## 9 fucking -4 9
## 10 damn -4 8
## 11 dick -4 7
## 12 asshole -4 1
## 13 bullshit -4 1
## 14 fucked -4 1
## 15 fucker -4 1
## 16 hell -4 1
## 17 pissed -4 1
## 18 lost -3 11
## 19 bad -3 4
## 20 goddamn -3 4
## 21 ugly -3 4
## 22 dead -3 3
## 23 die -3 3
## 24 hate -3 3
## 25 died -3 2acidrap_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
count(word, value, sort = TRUE) %>%
arrange(desc(value)) %>%
head(25)## word value n
## 1 win 4 1
## 2 love 3 59
## 3 lucky 3 3
## 4 beautiful 3 2
## 5 glad 3 2
## 6 hahaha 3 2
## 7 happy 3 2
## 8 nice 3 2
## 9 praise 3 2
## 10 classy 3 1
## 11 haha 3 1
## 12 happiness 3 1
## 13 joy 3 1
## 14 chance 2 59
## 15 hope 2 7
## 16 favorite 2 6
## 17 hug 2 6
## 18 hoping 2 3
## 19 save 2 3
## 20 loving 2 2
## 21 proud 2 2
## 22 avid 2 1
## 23 care 2 1
## 24 chances 2 1
## 25 clean 2 1acidrap_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value < 0) %>%
count()## n
## 1 362acidrap_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) %>%
filter(value > 0) %>%
count()## n
## 1 278With Acid Rap, one of the most used words, “love”, is overwhelmingly positive, but overall the album has much more negative words than positive. There were 362 negative words in comparison to 278 positive words in Acid Rap. It is not as polar as the other albums, but still more negative than positive.
The last step of this analysis is to determine the average sentiment of each album and compare the mean value of every word used.
thebigday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) -> thebigday_sentiment
mean(thebigday_sentiment$value)## [1] -0.02541296coloringbook_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) -> coloringbook_sentiment
mean(coloringbook_sentiment$value)## [1] 0.1338912tenday_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) -> tenday_sentiment
mean(tenday_sentiment$value)## [1] -1.528736acidrap_words %>%
anti_join(stop_words) %>%
inner_join(get_sentiments('afinn')) -> acidrap_sentiment
mean(acidrap_sentiment$value)## [1] -0.9046875Comparing the average sentiment of each album resulted in a similar conclusion that most fans had already assumed. 10 Day and Acid Rap are much more negative albums with an average sentiment of -1.5 and -0.9 respectively. The surprising outcome is that The Big Day landed just under 0 with a sentiment of -0.025. What was seen as an almost too positive album, had an overall average in the negative. Coloring Book followed what was expected with an average sentiment of 0.13.
When comparing the sentiment of each Chance the Rapper album, it can be seen that there were clear changes in his themes and overall positivity over time. The first two works he put out were significantly more negative than his two most recent projects. Fans have speculated over this for years, but the numbers back up the ideas of a new Chance the Rapper in comparison to the one who was trying to make it in the music industry. The clearest outliar would be the average sentiment of The Big Day, which was seen as an album that was too happy and too positive by the fans, and yet the average sentiment of the language used was in the negative.