Vampire Weekend Lyrics Analysis

Background

Vampire Weekend is an indie band that formed in 2006. Since then, they have released four albums. “Vampire Weekend” was released in 2008, then “Contra” in 2010, “Modern Vampires of the City” in 2013, and “Father of the Bride” in 2019. Over time, the overall tone and mood of the music and music videos became darker and less pop-y.

This report investigates the hypothesis that the lyrics overall became more negative over time

Method

Required Packages

library(tidyverse)
library(tidytext)
library(genius)
library(wordcloud2)
library(textdata)
library(scales)
library(ggthemes)

Using the Genius library to find and break down Vampire Weekend’s discography into lyrics by album

vw_albums <-tribble(
  ~artist, ~title,
  "Vampire Weekend", "Vampire Weekend",
  "Vampire Weekend", "Contra",
  "Vampire Weekend", "Modern Vampires of the City",
  "Vampire Weekend", "Father of the Bride",
)

vw_song_lyrics <- vw_albums %>% 
  add_genius(artist, title, type = "album")

Separating lyrics from lines into words for each album

First Album “Vampire Weekend”

vw_song_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
anti_join(stop_words) %>% 
  filter(title %in% "Vampire Weekend") %>% 
  count(word, sort = TRUE) ->vw_word_album1

Second Album “Contra”

vw_song_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  filter(title %in% "Contra") %>% 
  count(word, sort = TRUE) -> vw_word_album2

Removing the word “ooh” from “Contra”

The first wordcloud for “Contra” will show why this step was taken.

vw_song_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  filter(!word %in% "ooh") %>% 
    anti_join(stop_words) %>% 
  filter(title %in% "Contra") %>% 
  count(word, sort = TRUE) -> vw_word_album2_no_ooh

Third Album “Modern Vampires of the City”

vw_song_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  filter(title %in% "Modern Vampires of the City") %>% 
  count(word, sort = TRUE) -> vw_word_album3

Fourth Album “Father of the Bride”

vw_song_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  filter(title %in% "Father of the Bride") %>% 
  count(word, sort = TRUE) -> vw_word_album4

Most Common Words

Vampire Weekend

vw_word_album1 %>% 
wordcloud2(color='random-light')

vw_word_album1 %>% 
  head(10) ->album1top10

album1top10 %>% 
  ggplot(aes(reorder(word, -n), n, fill=word)) + geom_col() + scale_fill_tableau() + theme_economist() + ggtitle("Top 10 Words of 'Vampire Weekend'") + labs(y= "Number of Appearances", x = "Words") 

Contra

vw_word_album2 %>% 
  wordcloud2(color='random-light')

Contra Wordcloud Revisted

vw_word_album2_no_ooh %>% 
  wordcloud2(color='random-light')

This list excludes “ooh”

vw_word_album2_no_ooh  %>% 
  head(10) ->album2top10

album2top10 %>% 
  ggplot(aes(reorder(word, -n), n, fill=word)) + geom_col() + scale_fill_tableau() + theme_economist() + ggtitle("Top 10 Words of 'Contra'") + labs(y= "Number of Mentions", x = "Words") 

Modern Vampires of the City

vw_word_album3 %>% 
  wordcloud2(color='random-light')

vw_word_album3  %>% 
  head(10) ->album3top10

album3top10 %>% 
  ggplot(aes(reorder(word, -n), n, fill=word)) + geom_col() + scale_fill_tableau() + theme_economist() + ggtitle("Top 10 Words of 'Modern Vampires of the City'") + labs(y= "Number of Mentions", x = "Words") 

Father of the Bride

vw_word_album4 %>% 
  wordcloud2(color='random-light')

vw_word_album4  %>% 
  head(10) ->album4top10

album4top10 %>% 
  ggplot(aes(reorder(word, -n), n, fill=word)) + geom_col() + scale_fill_tableau() + theme_economist() + ggtitle("Top 10 Words of 'Father of the Bride'") + labs(y= "Number of Mentions", x = "Words") 

Sentiments

The sentiment analysis was accomplished with the Afinn lexicon. Afinn ranks words on a scale of -5 to 5 based on postive or negative connotation. -5 is the most negative rating and 5 is the most positive.

The tables below are the 10 most positive (left) and 10 most negative (right) words from each respective album. If multiple words have the same ranking in the lexicon, they are then ordered in the table based on frequency.

Vampire Weekend

vw_song_lyrics %>% 
  filter(title %in% "Vampire Weekend") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(value)) -> vw_word_album_afinn_pos

vw_word_album_afinn_pos %>% 
  head(10)

## # A tibble: 10 x 3
##    word        n value
##    <chr>   <int> <dbl>
##  1 funny       1     4
##  2 charm       2     3
##  3 praise      2     3
##  4 perfect     1     3
##  5 chance      3     2
##  6 fine        2     2
##  7 smile       2     2
##  8 tops        2     2
##  9 true        2     2
## 10 cares       1     2

vw_song_lyrics %>% 
  filter(title %in% "Vampire Weekend") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(-value)) -> vw_word_album_afinn_neg

vw_word_album_afinn_neg %>% 
  head(10)

## # A tibble: 10 x 3
##    word          n value
##    <chr>     <int> <dbl>
##  1 fuck          5    -4
##  2 shit          1    -4
##  3 cruel         3    -3
##  4 dumb          2    -3
##  5 evil          1    -3
##  6 lost          1    -3
##  7 murdering     1    -3
##  8 racist        1    -3
##  9 insane        6    -2
## 10 collapse      1    -2

There are 18 occurrences of the 10 unique most positive words. The median value is 2 and mean is 2.5.

There are 22 occurrences of the 10 unique most negative words. The mean and median values are both -3.

Contra

vw_song_lyrics %>% 
  filter(title %in% "Contra") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(value)) -> c_word_album_afinn_pos

c_word_album_afinn_pos %>% 
  head(10)

## # A tibble: 10 x 3
##    word       n value
##    <chr>  <int> <dbl>
##  1 funny      2     4
##  2 love       1     3
##  3 lovely     1     3
##  4 chance     3     2
##  5 care       2     2
##  6 honest     2     2
##  7 brave      1     2
##  8 enjoy      1     2
##  9 fair       1     2
## 10 fine       1     2

vw_song_lyrics %>% 
  filter(title %in% "Contra") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(-value)) -> c_word_album_afinn_neg

c_word_album_afinn_neg %>% 
  head(10)

## # A tibble: 10 x 3
##    word          n value
##    <chr>     <int> <dbl>
##  1 cruel         2    -3
##  2 lost          2    -3
##  3 worse         2    -3
##  4 bad           1    -3
##  5 desperate     1    -3
##  6 die           1    -3
##  7 fake          1    -3
##  8 horrified     1    -3
##  9 victim        1    -3
## 10 bitter        4    -2

There are 14 occurrences of the 10 unique most positive words. The median and mean values are both 2

There are 16 occurrences of the 10 unique most negative words. The mean median is -3 and the mean is -2.9.

Modern Vampires of the City

vw_song_lyrics %>% 
  filter(title %in% "Modern Vampires of the City") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(value)) -> mvotc_word_album_afinn_pos

mvotc_word_album_afinn_pos %>% 
  head(10)

## # A tibble: 10 x 3
##    word          n value
##    <chr>     <int> <dbl>
##  1 rejoicing     1     4
##  2 love         20     3
##  3 praise        4     3
##  4 excited       3     3
##  5 blessing      2     3
##  6 charming      1     3
##  7 luck          1     3
##  8 pleasant      1     3
##  9 won           1     3
## 10 stronger      4     2

vw_song_lyrics %>% 
  filter(title %in% "Modern Vampires of the City") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(-value)) -> mvotc_word_album_afinn_neg

mvotc_word_album_afinn_neg %>% 
  head(10)

## # A tibble: 10 x 3
##    word      n value
##    <chr> <int> <dbl>
##  1 damn      2    -4
##  2 hell      1    -4
##  3 die       8    -3
##  4 idiot     3    -3
##  5 died      2    -3
##  6 bad       1    -3
##  7 hate      1    -3
##  8 lost      1    -3
##  9 fire     14    -2
## 10 fool      5    -2

There are 39 occurrences of the 10 unique most positive words. The median and mean values are both 3.

There are 38 occurrences of the 10 unique most negative words. The mean and median values are both -3.

Father of the Bride

vw_song_lyrics %>% 
  filter(title %in% "Father of the Bride") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(value)) -> fotb_word_album_afinn_pos

fotb_word_album_afinn_pos %>% 
  head(10)

## # A tibble: 10 x 3
##    word          n value
##    <chr>     <int> <dbl>
##  1 win           5     4
##  2 funny         1     4
##  3 triumph       1     4
##  4 love          7     3
##  5 affection     4     3
##  6 grand         1     3
##  7 loved         1     3
##  8 perfect       1     3
##  9 sympathy      6     2
## 10 proud         4     2

vw_song_lyrics %>% 
  filter(title %in% "Father of the Bride") %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  arrange(desc(-value)) -> fotb_word_album_afinn_neg

fotb_word_album_afinn_neg %>% 
  head(10)

## # A tibble: 10 x 3
##    word         n value
##    <chr>    <int> <dbl>
##  1 die          8    -3
##  2 worried      4    -3
##  3 violence     3    -3
##  4 anger        2    -3
##  5 cruel        2    -3
##  6 evil         2    -3
##  7 fake         2    -3
##  8 hate         2    -3
##  9 kill         2    -3
## 10 lost         2    -3

There are 31 occurrences of the 10 unique most positive words. The median value is 3 and mean is 3.1.

There are 29 occurrences of the 10 unique most negative words. The mean and median values are both -3.

Conclusion

Frequency

Most of the top 10 most common words among the four albums are filler like “ooh” and “la” or simply have no connatation either way.

Sentiment Analysis

Of the 10 most positive words that appear in each individual album, the number of occurrences does not follow a clear negatively or positively sloped line. A line of best fit would be positively sloped as they go from 18 to 15 to 39 to 31. Meaning, the most postive words are used more often as time goes on generally.

As for negative words, the total occurrences go from 22 to 16 to 38 to 29. This follows the exact same pattern. It appears that language with strong connotations (be it positive or negative) tend to appear with each album.

The absolute values of the means and medians for all eight tables are very similar to each other as well. For each album, the absolute values of the medians and means of the negative rankings are greater than or equal to their positive counterparts in every instance but one. The exception is the last album. The absolute values for both medians are 3 and the absolute value for the negative mean is 3 and the absolute value for the positive mean is 3.1. Close, but not equal.

Because the data showed the same patterns for the rise of positive and negative words and overall similar outcomes for measures of central tendencies, the hypothesis has been disproved. While the lyrics have become more negative, they have also become more positive with time.