Taylor Swift lyrical EDA

Introduction

Ill be utilizing data visualization and statistical analysis methods and techniques to Taylor Swift lyrics(9 albums) to explore lyrical trends. She wrote “Speak Now” entirely by herself, one goal is to identify distinctive trends or an anomaly about “Speak Now”. With her two most recent albums, “folklore” and “evermore”, being labeled her “best written” albums by her fandom(Swifties) and completely different in mood/tone to the other albums in her catalog, a sentiment analysis could shine a light on that statement. I used SAS(Statistical Analysis Software) to break down lyric lines to words. After that I used the haven package to read in those sas data sets to r, besides that initial uses all other methods are done in r.

These are the albums and number of songs from each used in the data.

ALBUM SONGS
Taylor Swift 16
Fearless 26
Speak Now 17
Red 30
1989 16
Reputation 15
Lover 19
folklore 17
evermore 17
Midnights 22

Here are the libraries used in this markdown

#Load the libraries
                                    
library(readxl)      # reads xlsx and xls files
library(dplyr)       # SQL-style data processing
library(tidytext)    # text analysis in R
library(tidyr)       # data reshaping
library(plotly)      # plot making
library(wordcloud)   # generating wordclouds
library(ggplot2)     # generating plots 
library(forcats)     # handling variables
library(kableExtra)  # making graphs and tables
library(magrittr)    # increase efficiency and make more concise code
library(tidyverse)   # data science package collection
library(circlize)    # making circular graphs
library(haven)       # SAS data set reading
library(lubridate)

Data Cleaning

The data cleaning process began with SAS filtering/compressing and ended with R filtering the lyrics data.

SAS Code

SAS was used to compress lines of lyrics to single word strings. A character string of lines of song lyrics were read and compressed to a string of single word lyrics. These then were put in files with their associated album.

SAS read in an xlsx file with the data structured as below containing all her songs.

track_title track_n line lyric album
Tim McGraw 1 1 He said the way my blue eyes shined Taylor Swift
Tim McGraw 1 2 Put those Georgia stars to shame that night Taylor Swift
….. ….. ….. ….. …..
evermore (Ft. Bon Iver) 15 59 This pain wouldn’t be for evermore Evermore
evermore (Ft. Bon Iver) 15 60 Evermore Evermore 
#data Tswift.Albums_dat (keep = album_num track_n Lyric);
#set Tswift.Albums;

#        if album = 'Taylor Swift' then album_num = '1';
#...
#   else if album = 'Evermore'     then album_num = '9';
#run;


#data Tswift.TS1(drop = lyric st_count i )
#...
#    Tswift.TS9(drop = lyric st_count i )  ;
#   set Tswift.albums_dat;
#   length word $ 20;
    
#   st_count = countw(lyric);
        
#       do i = 1 to st_count;
#           st_count = countw(lyric);   
#           word = compress(scan(lyric,i), "'", 'k a d');
#word = propcase(word);

#               select(album_num);
#                   when('1') OUTPUT Tswift.TS1;
#...        
#                   when('9') OUTPUT Tswift.TS9;
#               end;
#       end;
#run;

SAS Data files look like this.

track_n album_num word
1 1 He
1 1 Said
1 1 The
1 1 Way
1 1 My
1 1 Blue
… … …
15 1 Won’t
15 1 See

Stop words and other non-unique or non-sentient associated words were removed. These stop words were attained from the tidytext package with its stop words data set. Using anti_join, the two data sets were merged and only non-stop words were taken. Lyrics that I consider stop words but the tidytext package did not include were removed manually.

df1 <- readxl::read_xlsx("new.xlsx")

df1$time <- df1$m + df1$s/60

ds1 <- df1[df1$album_num == 1, ]
ds2 <- df1[df1$album_num == 2, ]
ds3 <- df1[df1$album_num == 3, ]
ds4 <- df1[df1$album_num == 4, ]
ds5 <- df1[df1$album_num == 5, ]
ds6 <- df1[df1$album_num == 6, ]
ds7 <- df1[df1$album_num == 7, ]
ds8 <- df1[df1$album_num == 8, ]
ds9 <- df1[df1$album_num == 9, ]
ds10 <- df1[df1$album_num ==10, ]
# Counts the number of each word

f1 <- ds1 %>% 
  mutate(word = as.character(word))
f2 <- ds2 %>% 
  mutate(word = as.character(word))
f3 <- ds3 %>% 
  mutate(word = as.character(word))
f4 <- ds4 %>% 
  mutate(word = as.character(word))
f5 <- ds5 %>% 
  mutate(word = as.character(word))
f6 <- ds6 %>% 
  mutate(word = as.character(word))
f7 <- ds7 %>% 
  mutate(word = as.character(word))
f8 <- ds8 %>% 
  mutate(word = as.character(word))
f9 <- ds9 %>% 
  mutate(word = as.character(word))
f10 <- ds10 %>% 
  mutate(word = as.character(word))




ftot <- bind_rows(f1,f2,f3,f4,f5,f6,f7,f8,f9,f10)

  ds1$word <- tolower(ds1$word)
  ds2$word <- tolower(ds2$word)
  ds3$word <- tolower(ds3$word)
  ds4$word <- tolower(ds4$word)
  ds5$word <- tolower(ds5$word)
  ds6$word <- tolower(ds6$word)
  ds7$word <- tolower(ds7$word)
  ds8$word <- tolower(ds8$word)
  ds9$word <- tolower(ds9$word)
  ds10$word <- tolower(ds10$word)

  # creating data frames to hold unique words 
  
ds1 <- ds1 %>%
 anti_join(stop_words,  c("word" = "word"))
ds2 <- ds2 %>%
 anti_join(stop_words,  c("word" = "word"))
ds3 <- ds3 %>%
 anti_join(stop_words,  c("word" = "word"))
ds4 <- ds4 %>%
 anti_join(stop_words,  c("word" = "word"))
ds5 <- ds5 %>%
 anti_join(stop_words,  c("word" = "word"))
ds6 <- ds6 %>%
 anti_join(stop_words,  c("word" = "word"))
ds7 <- ds7 %>%
 anti_join(stop_words,  c("word" = "word"))
ds8 <- ds8 %>%
 anti_join(stop_words,  c("word" = "word"))
ds9 <- ds9 %>%
 anti_join(stop_words,  c("word" = "word"))  
ds10 <- ds10 %>%
 anti_join(stop_words,  c("word" = "word"))  

# Counts the number of each word
freq1 <- ds1 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq2 <- ds2 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq3 <- ds3 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq4 <- ds4 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq5 <- ds5 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq6 <- ds6 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq7 <- ds7 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq8 <- ds8 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq9 <- ds9 %>% 
  mutate(word = as.character(word)) %>%
  count(word)
freq10 <- ds10 %>% 
  mutate(word = as.character(word)) %>%
  count(word)

# orders the words by count
freq1 <- freq1[order(-freq1$n),]
freq2 <- freq2[order(-freq2$n),]
freq3 <- freq3[order(-freq3$n),]
freq4 <- freq4[order(-freq4$n),]
freq5 <- freq5[order(-freq5$n),]
freq6 <- freq6[order(-freq6$n),]
freq7 <- freq7[order(-freq7$n),]
freq8 <- freq8[order(-freq8$n),]
freq9 <- freq9[order(-freq9$n),]
freq10 <- freq10[order(-freq10$n),]

# creates a master list of all the words in her albums with count
freqtot <- bind_rows(ds1,ds2,ds3,ds4,ds5,ds6,ds7,ds8,ds9,ds10)
freqtot1 <- freqtot %>% 
  mutate(word = as.character(word))%>%
  count(word)

# removes some words manually
freqtot <- freqtot %>% 
  anti_join(stop_words, by=c("word"="word")) %>% 
  filter(word != "ooh") %>% 
  filter(word != "yeah") %>% 
  filter(word != "ah") %>% 
  filter(word != "uh") %>% 
  filter(word != "ha") %>%
  filter(word != "whoa") %>%
  filter(word != "eh") %>%
  filter(word != "hoo") %>%
  filter(word != "ey") %>%
  filter(word != "mmm") %>% 
  filter(word != "eeh") %>% 
  filter(word != "huh") %>% 
  filter(word != "na") %>%
  filter(word != "di")%>%
  filter(word != "la")%>%
  filter(word != "aah")%>%
  filter(word != "ra")%>%
  filter(word != "haa")%>%
  filter(word != "di")%>%
  filter(word != "da")%>%
  filter(word != "'cause")%>%
  filter(word != "gonna")%>%
  filter(word != "The")%>%
  filter(word != "wanna")

freqtot1 <- freqtot %>% 
  mutate(word = as.character(word))%>%
  count(word)

A table of word count for all unique words in the albums

word n
love 281
time 263
baby 153
night 128
home 95
stay 95
eyes 89
feel 89
remember 87
run 86
girl 82
call 81
bad 71
life 67
heart 66
friends 62
day 60
leave 59
red 58
break 57
mind 57
shake 57
forever 55
lights 49
miss 49
lost 48
smile 48
beautiful 47
hey 46
town 46
car 45
hand 45
mine 45
waiting 45
left 44
talk 44
dancing 43
fall 43
light 43
hands 42
hold 42
door 41
hope 41
daylight 40
walk 39
meet 38
dreams 37
hate 37
hear 37
mm 37
people 37
watch 37
babe 36
coming 36
wrong 36
feeling 35
mad 35
would’ve 35
dress 34
forget 34
head 34
kiss 34
stop 34
woah 34
world 34
dark 33
fight 33
loved 33
nice 33
street 33
’til 32
dream 32
lose 32
heard 31
rain 31
blue 30
live 30
york 30
bet 29
darling 29
hard 29
should’ve 29
trouble 29
twenty 29
breathe 28
days 28
fine 28
fun 28
hair 28
song 28
wait 28
boy 27
change 27
fly 27
game 27
phone 27
soul 27
drive 26
honey 26
middle 26
touch 26
anymore 25
body 25
happy 25
late 25
met 25
past 25
play 25
sad 25
times 25
wake 25
woods 25
could’ve 24
found 24
house 24
shine 24
sky 24
tonight 24
front 23
goodbye 23
guess 23
someday 23
alright 22
girls 22
ground 22
real 22
save 22
single 22
snow 22
true 22
words 22
cold 21
comin’ 21
fake 21
feels 21
follow 21
picture 21
standing 21
story 21
told 21
walked 21
cool 20
dance 20
grow 20
makes 20
moment 20
shit 20
silence 20
sweet 20
thinking 20
window 20
’bout 19
dead 19
god 19
line 19
nights 19
pain 19
shame 19
summer 19
tied 19
tired 19
’em 18
city 18
deep 18
easy 18
eye 18
fell 18
jump 18
laughing 18
lips 18
looked 18
morning 18
screaming 18
stars 18
sun 18
beat 17
blood 17
crazy 17
floor 17
free 17
held 17
promises 17
reason 17
sit 17
sleep 17
stand 17
tears 17
’round 16
beach 16
burn 16
burning 16
called 16
clean 16
close 16
karma 16
party 16
perfect 16
pretty 16
read 16
road 16
scared 16
team 16
wonderland 16
bright 15
broke 15
cry 15
feet 15
friend 15
getaway 15
laugh 15
losin’ 15
lucky 15
sick 15
talking 15
wishing 15
worst 15
bed 14
delicate 14
fairytale 14
falling 14
games 14
gorgeous 14
gotta 14
gray 14
kitchen 14
nothin’ 14
starlight 14
start 14
step 14
string 14
trust 14
white 14
wind 14
worse 14
bless 13
boys 13
breath 13
brought 13
caught 13
cut 13
damn 13
dancin’ 13
died 13
doin’ 13
fancy 13
favorite 13
fifteen 13
finally 13
hits 13
magic 13
moved 13
paint 13
rest 13
running 13
sign 13
simple 13
sing 13
standin’ 13
stood 13
watched 13
wondering 13
2 12
black 12
burned 12
bye 12
catch 12
crying 12
december 12
die 12
happiness 12
hit 12
hurt 12
inside 12
kid 12
kill 12
learned 12
low 12
memories 12
mess 12
midnight 12
paper 12
pretend 12
pulled 12
school 12
shirt 12
signs 12
speak 12
stairs 12
swear 12
truth 12
walking 12
wild 12
air 11
arms 11
battle 11
begin 11
blind 11
breaking 11
broken 11
chance 11
cornelia 11
dear 11
drunk 11
fire 11
flying 11
funny 11
gold 11
golden 11
hallway 11
hide 11
insane 11
list 11
london 11
lord 11
loving 11
marvelous 11
missing 11
money 11
perfectly 11
plans 11
ready 11
scene 11
somethin’ 11
stayed 11
stupid 11
takes 11
throw 11
walls 11
war 11
wide 11
wine 11
afraid 10
bar 10
begging 10
belong 10
blame 10
bought 10
bring 10
care 10
champagne 10
changed 10
cheeks 10
closure 10
cried 10
death 10
drug 10
faith 10
fast 10
fighting 10
green 10
hang 10
lot 10
lover 10
mark 10
million 10
passed 10
peace 10
pictures 10
pieces 10
playing 10
prove 10
question 10
rains 10
rare 10
reputation 10
sitting 10
space 10
started 10
taking 10
thinkin’ 10
threw 10
understand 10
usin’ 10
water 10
weekend 10
wildest 10
word 10
worth 10
ya 10
band 9
covered 9
dreaming 9
evermore 9
giving 9
grown 9
guy 9
haze 9
heartbeat 9
heaven 9
hell 9
hopin’ 9
hoping 9
hurts 9
lie 9
livin’ 9
losing 9
loud 9
makin’ 9
months 9
move 9
page 9
pick 9
promise 9
secret 9
starts 9
storm 9
straight 9
taylor 9
undone 9
warning 9
win 9
woman 9
write 9
1 8
american 8
believed 8
bottle 8
busy 8
calm 8
conversation 8
cross 8
cruel 8
cuts 8
daydream 8
drew 8
drop 8
family 8
fears 8
feelin’ 8
flew 8
glass 8
grew 8
haunted 8
heartbreak 8
hearts 8
ho 8
keeping 8
knees 8
lead 8
leaving 8
loves 8
lying 8
mistakes 8
pocket 8
push 8
queen 8
ran 8
regret 8
revenge 8
roll 8
rush 8
shaking 8
shiny 8
sight 8
slow 8
smiles 8
songs 8
sparks 8
spot 8
strike 8
style 8
supposed 8
thousand 8
tryna 8
wear 8
worship 8
alive 7
bigger 7
brand 7
chain 7
child 7
choice 7
clothes 7
color 7
count 7
counting 7
crowd 7
dorothea 7
drink 7
faster 7
fearless 7
flames 7
flashback 7
forgot 7
garden 7
half 7
happened 7
honestly 7
impossible 7
innocent 7
island 7
joke 7
keys 7
kinda 7
king 7
lavender 7
lived 7
lives 7
lookin’ 7
maroon 7
marry 7
mirror 7
moon 7
nobody’s 7
pouring 7
pride 7
quiet 7
reader 7
realize 7
realized 7
ridin’ 7
ring 7
rings 7
rumors 7
runnin’ 7
sayin’ 7
scar 7
seat 7
sense 7
shined 7
short 7
singing 7
skin 7
sound 7
spend 7
spinning 7
stare 7
strange 7
strong 7
tall 7
truck 7
view 7
warm 7
wasted 7
whisper 7
age 6
ago 6
agrees 6
ashes 6
block 6
bones 6
brave 6
brighter 6
castle 6
casually 6
catching 6
chasing 6
closer 6
club 6
crowded 6
cursed 6
daughter 6
dressed 6
figured 6
film 6
flowers 6
freedom 6
frozen 6
ghost 6
goin’ 6
grace 6
heels 6
holding 6
invisible 6
jeans 6
jokes 6
kids 6
kingdom 6
learn 6
letter 6
lies 6
lines 6
lonely 6
lovers 6
lovin’ 6
message 6
midnights 6
minds 6
moves 6
movie 6
music 6
neck 6
note 6
notice 6
pass 6
pay 6
picked 6
played 6
polish 6
pretenders 6
radio 6
reasons 6
reminds 6
rocks 6
rose 6
sacred 6
scarlet 6
scars 6
scream 6
secrets 6
shade 6
shape 6
skies 6
someone’s 6
stories 6
sunshine 6
survived 6
swift 6
taught 6
thðµ 6
tight 6
train 6
treacherous 6
trees 6
voice 6
vow 6
waking 6
waves 6
wearing 6
whispered 6
windows 6
wound 6
wrapped 6
3 5
actress 5
affair 5
assume 5
attitude 5
autumn 5
bathroom 5
bedroom 5
beneath 5
bent 5
betty 5
blew 5
blues 5
bone 5
boyfriend 5
breakin’ 5
breathing 5
buttons 5
careless 5
cars 5
chase 5
chest 5
crashing 5
crime 5
crowds 5
cryin’ 5
cursing 5
desk 5
dinner 5
drama 5
drinking 5
driving 5
dropped 5
drowning 5
dust 5
dying 5
enchanted 5
eyed 5
faded 5
fallin’ 5
fate 5
father 5
fear 5
footsteps 5
forgiveness 5
frames 5
fuckin’ 5
gettin’ 5
ghosts 5
glitch 5
goddamn 5
grab 5
guiding 5
guys 5
hallelujah 5
hanging 5
harder 5
haunt 5
hole 5
how’d 5
hundred 5
james 5
karma’s 5
killing 5
knife 5
knocked 5
ladies 5
land 5
lasts 5
leads 5
leaves 5
likes 5
listen 5
listening 5
liveget 5
lock 5
mastermind 5
matter 5
meant 5
memory 5
movin’ 5
names 5
news 5
ocean 5
one’s 5
pacing 5
painted 5
paris 5
parties 5
plan 5
porch 5
prayer 5
price 5
prince 5
pull 5
pushed 5
queens 5
rainy 5
reached 5
recall 5
reckless 5
recognize 5
ride 5
romantics 5
romeo 5
ruining 5
rules 5
sadness 5
saint 5
screams 5
screen 5
shining 5
shoes 5
sleeve 5
solve 5
stephen 5
stranger 5
swing 5
talks 5
taste 5
tea 5
telling 5
tickets 5
toast 5
top 5
tossing 5
trace 5
tragic 5
tryin’ 5
twin 5
understands 5
waited 5
waitin’ 5
wall 5
weather 5
whispers 5
why’d 5
winter 5
woke 5
wore 5
writing 5
wrote 5
wðµ 5
111you 4
16th 4
4 4
ache 4
advice 4
afternoon 4
ages 4
amount 4
angel 4
apartment 4
asleep 4
avenue 4
awake 4
ball 4
boat 4
born 4
breaks 4
breeze 4
bricks 4
bride 4
brother 4
build 4
built 4
cab 4
careful 4
cat 4
chill 4
christmas 4
clover 4
coat 4
colors 4
combat 4
comfortable 4
confess 4
confused 4
corner 4
counted 4
counterfeit 4
crimson 4
crown 4
dad 4
daddy’s 4
danced 4
dare 4
darkest 4
deal 4
devil 4
diamonds 4
dim 4
dog 4
drawer 4
drivin’ 4
echoes 4
este’s 4
fade 4
false 4
figure 4
fit 4
fix 4
flash 4
flawless 4
flight 4
fuck 4
furious 4
gate 4
gown 4
guitar 4
headlights 4
hero 4
hiding 4
history 4
holdin’ 4
hometown 4
honest 4
horse 4
hummin’ 4
hunters 4
idea 4
infidelity 4
ing 4
internet 4
july 4
key 4
kissed 4
laid 4
laughed 4
lessons 4
lip 4
lipstick 4
living 4
lotta 4
love’s 4
magical 4
mall 4
man’s 4
map 4
match 4
mcgraw 4
mirrorball 4
misery 4
mistake 4
month 4
mother 4
motion 4
moving 4
nasty 4
pageant 4
pages 4
paradise 4
park 4
patch 4
pickin’ 4
piece 4
players 4
pool 4
pray 4
proof 4
remind 4
risk 4
rolled 4
roof 4
ruin 4
saved 4
scarf 4
sea 4
search 4
serve 4
set 4
seventeen 4
shattered 4
shimmer 4
ships 4
shot 4
sidewalk 4
sittin’ 4
skip 4
skippin’ 4
skipping 4
slamming 4
sleeping 4
slope 4
smell 4
sneakin’ 4
soundtrack 4
spelling 4
starin’ 4
staring 4
stick 4
stone 4
stopped 4
strangers 4
streets 4
stronger 4
sunny 4
superman 4
switch 4
swore 4
takin’ 4
teach 4
teatime 4
ten 4
terrified 4
tim 4
tragedy 4
tricks 4
trip 4
tuesday 4
tune 4
untouchable 4
walkin’ 4
watching 4
week 4
west 4
wife 4
women 4
wreck 4
yesterday 4
’fore 3
20 3
29th 3
2am 3
act 3
admit 3
ahead 3
aisle 3
album 3
answer 3
applause 3
april 3
ayy 3
baby’s 3
bare 3
beating 3
bein’ 3
bench 3
birthday 3
bit 3
blank 3
bleachers 3
bleed 3
blink 3
bloodshed 3
blow 3
book 3
breathless 3
bulletproof 3
burgundy 3
burnin’ 3
cafã 3
cages 3
calling 3
canceled 3
card 3
carry 3
ceiling 3
chair 3
changing 3
check 3
choices 3
choose 3
chorus 3
chose 3
church 3
circus 3
clappin’ 3
classic 3
clever 3
coast 3
coffee 3
colder 3
collarbone 3
complicated 3
consequence 3
couch 3
counter 3
cover 3
crashed 3
crawling 3
creeping 3
curve 3
daddy 3
date 3
dean 3
decide 3
defending 3
deserve 3
dice 3
dies 3
dirty 3
disappear 3
ditch 3
dominoes 3
doors 3
double 3
doubt 3
drag 3
drawing 3
dreamed 3
dressin’ 3
drives 3
easier 3
eighteen 3
empty 3
enchanting 3
enemies 3
enjoy 3
escape 3
everyone’s 3
exes 3
existed 3
fades 3
fair 3
faking 3
falls 3
fame 3
familiar 3
fantasy 3
farm 3
feelings 3
fences 3
field 3
fights 3
filled 3
fireplace 3
fits 3
flashbacks 3
flashing 3
flaws 3
floors 3
flush 3
flyin’ 3
folk 3
fool 3
football 3
force 3
forevermore 3
forward 3
fought 3
foxes 3
fragile 3
frame 3
girlfriend 3
glad 3
glance 3
glow 3
groundwork 3
growing 3
gun 3
haha 3
hall 3
haters 3
holiday 3
hung 3
imagined 3
impress 3
incredible 3
indifference 3
ivy 3
jealous 3
jet 3
juliet 3
jumping 3
keepin’ 3
kings 3
kisses 3
kissing 3
knives 3
knowing 3
lady 3
lame 3
laughin’ 3
law 3
lay 3
led 3
legacy 3
letters 3
lettin’ 3
lit 3
locked 3
locket 3
loose 3
lovely 3
lyin’ 3
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luxuries 1
luxury 1
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nicer 1
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nick 1
nightfall 1
nightmare 1
nightstand 1
nikes 1
nineteen 1
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ninety 1
ninth 1
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noose 1
normal 1
nose 1
notch 1
noticed 1
notices 1
novelty 1
now’ 1
nurses 1
oasis 1
oath 1
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obsession 1
obsessive 1
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occurring 1
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oil 1
olive 1
omen 1
one’ll 1
opacity 1
opal 1
opinion 1
optimism 1
orange 1
organ 1
organic 1
other’s 1
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patrãn 1
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penny 1
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peter 1
petty 1
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phase 1
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phoenix 1
phones 1
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photographs 1
photos 1
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picks 1
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picturing 1
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pile 1
pin 1
pine 1
pirate 1
pirates 1
piss 1
pitchforks 1
pjs 1
planets 1
planning 1
plant 1
plates 1
playboy 1
playful 1
plays 1
playthings 1
pleaser 1
plotted 1
podium 1
poem 1
poems 1
poets 1
poke 1
poker 1
policy 1
politðµ 1
pools 1
popcorn 1
poppy 1
portal 1
portrait 1
postcard 1
potential 1
potion 1
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pour 1
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prefer 1
prep 1
pressed 1
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prettiest 1
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priceless 1
prices 1
princesses 1
priorities 1
prison 1
privacy 1
prize 1
prizes 1
promised 1
promising 1
proportion 1
prose 1
protect 1
pub 1
pulling 1
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punch 1
punched 1
punching 1
punish 1
punished 1
purple 1
purring 1
pushing 1
puttin’ 1
puzzles 1
pãrignon 1
quicksand 1
quietly 1
quo 1
race 1
radiance 1
raidðµd 1
rainbow 1
rained 1
rainstorm 1
raised 1
raking 1
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ranting 1
raw 1
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reads 1
reality 1
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rearview 1
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reclaim 1
reconnected 1
redhead 1
reel 1
reflected 1
reflection 1
refused 1
regretting 1
regulars 1
reinvention 1
relate 1
related 1
relevant 1
relief 1
religion 1
religion’s 1
remembered 1
remembering 1
reminiscing 1
removed 1
rep 1
repeating 1
replay 1
replayin’ 1
reply 1
reports 1
reputations 1
request 1
respects 1
responsibility 1
restless 1
restore 1
return 1
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revelers 1
revolved 1
rewind 1
rhode 1
rice 1
rich 1
riddles 1
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rifle 1
righteous 1
rip 1
rise 1
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robbers 1
rock 1
rode 1
rodeo 1
rogue 1
role 1
roles 1
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rosy 1
rub 1
rubies 1
rudely 1
rugby 1
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ruthless 1
saddest 1
sails 1
sake 1
saltbox 1
salute 1
sand 1
sang 1
sapphire 1
sashay 1
saturn 1
saucers 1
saving 1
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scent 1
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scooter 1
scoreboard 1
scorpion 1
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scrutiny 1
seal 1
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seats 1
secrecy 1
seek 1
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senior 1
sensual 1
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sequin 1
sets 1
sexy 1
shady 1
shaky 1
shameless 1
shaped 1
sharks 1
sharðµ 1
shatter 1
shift 1
shifting 1
shimmering 1
shines 1
shiniest 1
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shipwrecked 1
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shore 1
shoreditch 1
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shrink 1
shy 1
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sideshow 1
sidewalks 1
sigh 1
sights 1
signal 1
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simply 1
simplðµ 1
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situation 1
situations 1
situationship 1
sixties 1
skateboard 1
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skeptics 1
skirts 1
slates 1
slay 1
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sleepers 1
sleeps 1
sleeves 1
sleight 1
slight 1
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smiling 1
smooth 1
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snap 1
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soaking 1
soaring 1
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sober 1
socal 1
soho 1
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somebody’s 1
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wool 1
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wðµdnesday 1
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yelling 1
yeugh 1
yogurt 1
you’rðµ 1
youth 1
ðµverybody 1

Statistics

6 “positive” and 6 “sad” word counts are looked at throughout the albums.Sad sentiment related words are bad, mad, leave, hate, lost, fall. Positive words are love, baby, beautiful, hope, smile,kiss. Noticeable spike in “Love” for 1989, could be due to two tracks, “You are in Love” and “This Love”, making heavy use of the word. Aside from “Love”, “Baby”, and “Bad”, all other words stay under 25 times per album. “Baby” has noticeable trend increase in the time of 1989->Reputation->Lover, Reputation being the odd album out by being a more negative and an atypical album that we’ve seen from Ms. Swift.

Total number of words plotted by track, color associated with the album.

Total number of unique words plotted by track, color associated with the album.

The make up of unique words per song is calculated as a percentage and represented against the total unique word count for the tracks.

Here is a table for the unique word counts for all tracks.
Album Track Number Unique Word Count
1 1 89
1 2 80
1 3 53
1 4 34
1 5 56
1 6 16
1 7 47
1 8 61
1 9 42
1 10 83
1 11 94
1 12 44
1 13 27
1 14 30
1 16 57
2 1 68
2 2 87
2 3 90
2 4 83
2 5 60
2 6 84
2 7 69
2 8 56
2 9 50
2 10 73
2 11 77
2 12 98
2 13 63
2 14 92
2 15 70
2 17 44
2 18 63
2 19 70
2 20 55
2 21 69
2 22 97
2 23 60
2 24 50
2 25 24
2 26 56
3 1 93
3 2 81
3 3 107
3 4 107
3 5 99
3 6 69
3 7 75
3 8 101
3 9 98
3 10 78
3 11 75
3 12 51
3 13 98
3 14 139
3 15 86
3 16 53
3 17 87
4 1 74
4 2 117
4 3 72
4 4 103
4 5 118
4 6 115
4 7 42
4 8 65
4 9 83
4 10 76
4 11 55
4 12 78
4 13 67
4 14 69
4 15 98
4 16 81
4 17 70
4 18 52
4 19 63
4 21 82
4 22 54
4 23 89
4 24 92
4 25 85
4 26 117
4 27 79
4 28 99
4 29 59
4 30 260
5 1 85
5 2 140
5 3 93
5 4 86
5 5 69
5 6 158
5 7 57
5 8 82
5 9 86
5 10 64
5 11 93
5 12 67
5 13 83
5 14 104
5 15 105
5 16 132
6 1 99
6 2 162
6 3 88
6 4 139
6 5 78
6 6 59
6 7 74
6 8 96
6 9 129
6 10 134
6 11 126
6 12 80
6 13 92
6 14 114
6 15 78
7 1 72
7 2 96
7 3 59
7 4 70
7 5 62
7 6 93
7 7 140
7 8 118
7 9 116
7 10 98
7 11 142
7 12 39
7 13 74
7 14 74
7 15 60
7 16 80
7 17 60
7 18 123
7 19 101
8 1 63
8 2 83
8 3 121
8 4 96
8 5 81
8 6 52
8 7 68
8 8 79
8 9 49
8 10 79
8 11 96
8 12 71
8 13 54
8 14 86
8 15 60
8 16 62
8 17 56
9 1 76
9 2 104
9 3 97
9 4 84
9 5 76
9 6 89
9 7 103
9 8 64
9 9 98
9 10 99
9 11 78
9 12 105
9 13 65
9 14 49
9 15 106
9 16 144
9 17 82
10 1 76
10 2 114
10 3 91
10 4 114
10 5 92
10 6 70
10 7 95
10 8 69
10 9 101
10 10 43
10 11 101
10 12 78
10 13 84
10 14 119
10 15 49
10 16 77
10 17 82
10 18 55
10 19 104
10 20 87
10 21 123
10 22 122

The following 4 plots show that the Word count per minute and Unique word counts per minute are roughly Normally distributed. ## Wordcloud

Words associated with positive and sadness sentiments are used to make a word cloud. Thus giving an informative visual on the words most closely associated with these two sentiments. Positive sentiments being in red and sadness sentiments in violet.

Here the total count frequency of each word is represented by size and color. Love is the big winner sitting pretty in the middle, “Stay” and “Red” stand out to me because I want to hypothesize “Stay Stay Stay” and “Red” contribute heavily to their placement in the wordcloud.

Staying with the positive vs sad sentiment related words, the count frequency are represented with red for positive words and violet for sad words. Dear and Clean are words categorized into positive high frequency words but with songs like “Dear John” and “Clean” that would be categorized as sad or non-positive songs, giving me a future idea to label word sentiments based on the song sentiment rather than the words general sentiment.

Chord Diagram

Her 3 Grammy award winning Album of the Years are shown in the chord diagram along with the sentiments related to each album.

##             joy    positive anticipation   surprise      trust      anger
## [1,]  0.7869489  0.48314933   1.93319201  1.2653969  1.6257460 -0.5354847
## [2,] -0.4489954 -0.64985429   1.22619608 -0.8230518  0.1545289  3.2901173
## [3,]  0.3234698  0.05827297  -0.65912642  0.9173221 -0.3358768 -1.0136849
## [4,] -0.7579815 -1.07473065  -0.42346111 -0.1269023 -1.0714854 -0.7745848
## [5,] -0.2945024 -0.22497793   0.04786952 -0.8230518  0.1545289 -0.0572844
## [6,] -1.0669676 -1.35798156  -1.36612236 -1.1711266 -1.3166882 -0.7745848
##         disgust       fear   negative    sadness
## [1,]  0.2615317 -0.5182033 -0.8499267  0.2967685
## [2,]  2.8738251  1.9031360  2.0440109  0.9724152
## [3,] -0.6092328 -1.1785686 -1.0107010 -1.0545251
## [4,] -0.6092328 -0.9584468 -1.0107010 -0.8293095
## [5,]  0.8420413 -0.2980815  0.4362678  0.2967685
## [6,] -0.6092328 -0.7383250 -1.0107010 -0.6040939

## K-means clustering with 5 clusters of sizes 54, 67, 15, 17, 39
## 
## Cluster means:
##          joy   positive anticipation   surprise      trust      anger
## 1 -0.4776052 -0.4557750   -0.2096167 -0.1140106 -0.4176111  0.4474825
## 2 -0.5158655 -0.5378222   -0.5430525 -0.4490013 -0.5261835 -0.6104265
## 3  0.3646680  0.5020327    0.6291773  0.4068124  0.6285877  2.3815369
## 4  2.0410689  1.7744402    1.6420760  1.1630219  1.5968986 -0.3526434
## 5  0.5175765  0.5884606    0.2654067  0.2657975  0.5443385 -0.3331692
##      disgust       fear   negative    sadness
## 1  0.3690335  0.4519630  0.5762010  0.3801816
## 2 -0.5485825 -0.6923295 -0.6699555 -0.6040939
## 3  2.3320161  2.2846804  2.1511938  2.2636513
## 4 -0.2336089 -0.2462882 -0.3392318 -0.1271668
## 5 -0.3636325 -0.2077752 -0.3263796 -0.3038064
## 
## Clustering vector:
##   [1] 4 3 2 2 1 2 5 5 2 2 2 2 2 2 2 2 2 5 5 2 2 5 1 2 1 2 5 1 4 2 2 5 1 2 2 1 4
##  [38] 2 2 2 1 5 5 4 1 2 1 4 5 5 2 1 5 1 5 2 4 1 1 1 1 2 1 2 2 3 5 5 5 4 2 1 2 5
##  [75] 2 2 5 2 1 1 2 2 1 2 2 3 2 3 2 1 2 3 2 1 2 1 4 2 4 5 5 5 1 1 1 3 2 2 1 3 1
## [112] 2 3 2 5 2 2 1 3 5 1 1 4 3 5 5 1 4 2 4 1 1 4 2 1 5 5 5 4 1 1 2 1 4 2 1 5 3
## [149] 1 5 5 1 2 1 5 5 1 1 1 4 2 4 1 2 1 2 5 3 1 2 1 1 5 5 2 2 1 1 5 2 5 2 2 3 2
## [186] 2 3 2 3 5 1 1
## 
## Within cluster sum of squares by cluster:
## [1] 214.3078 148.8793 161.9063 183.6512 175.3873
##  (between_SS / total_SS =  53.7 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

##   cluster track_num       joy  positive anticipation surprise     trust
## 1       1  9.296296  4.814815  8.370370     4.907407 3.037037  3.666667
## 2       2 12.447761  4.567164  7.791045     3.492537 2.074627  3.223881
## 3       3 10.533333 10.266667 15.133333     8.466667 4.533333  7.933333
## 4       4 10.411765 21.117647 24.117647    12.764706 6.705882 11.882353
## 5       5  9.948718 11.256410 15.743590     6.923077 4.128205  7.589744
##       anger   disgust      fear  negative   sadness Album
## 1  6.111111  4.370370  7.407407 12.870370  7.370370    NA
## 2  1.686567  1.208955  2.208955  5.119403  3.000000    NA
## 3 14.200000 11.133333 15.733333 22.666667 15.733333    NA
## 4  2.764706  2.294118  4.235294  7.176471  5.117647    NA
## 5  2.846154  1.846154  4.410256  7.256410  4.333333    NA