Pull data

Here we pull data from Spotify, keeping just the four main albums in Spotify: Fleet Foxes, Helplessness Blues, Crack-Up, and Shore, which was just released in October 2020.

## Spotify data
ff <- get_artist_audio_features("fleet foxes") %>%
    select(1:3, track_name, 6, 9:20, 32, 36:39) %>%
    relocate(artist_name, track_name, album_name)

ff2 <- ff %>%
    filter(!album_id %in% c("2m7zr13OtqMWyPkO32WRo0", "5GRnydamKvIeG46dycID6v",
                            "6XzZ5pg9buAKNYg293KOQ8", "62miIQWlOO88YmupzmUNGJ",
                            "6ou9sQOsIY5xIIX417L3ud", "7LKzVm90JnhNMPF6qX21fS",
                            "7D0rCfJjFj9x0bdgRKtvzb")) %>%
    mutate(track_name_clean = str_to_lower(track_name),
           track_name_clean = str_replace_all(track_name_clean, "[:punct:]", " "))

The code chunk below uses the geniusr package to pull lyrics.

## Genus lyrics data
ff_id <- search_artist("Fleet Foxes") %>%
    pull(artist_id)

ff_songs <- get_artist_songs_df(ff_id)

ff_lyrics <- map(ff_songs$song_id, get_lyrics_id) %>%
    bind_rows()

ff_lyrics2 <- ff_lyrics %>%
    select(song_name, line, song_id) %>%
    group_by(song_id, song_name) %>%
    dplyr::summarize(line = paste0(line, collapse = " ")) %>%
    filter(!str_detect(song_name, "Booklet")) %>%
    mutate(song_name_clean = str_to_lower(song_name),
           song_name_clean = str_replace_all(song_name_clean, "[:punct:]", " "))

## `summarise()` regrouping output by 'song_id' (override with `.groups` argument)

## Alternative way to get lyrics using the genius package
# albums <- c("Shore", "Crack Up", "Helplessness Blues", "Fleet Foxes")
# ff_lyrics <- map(albums, ~genius_album(artist = "Fleet Foxes", album = .)) %>%
#     bind_rows()

Finally, we can combine the song features and lyrics into a single data frame.

## Combine lyrics and song features

album_order <- c("Shore",  "Crack-Up", "Helplessness Blues",  "Fleet Foxes")


ff_combined <- ff2 %>%
    left_join(ff_lyrics2, by = c("track_name_clean" = "song_name_clean")) %>%
    select(-track_name_clean, song_name) %>%
    filter(!is.na(line)) %>%
    mutate(album_name = factor(album_name, levels = album_order)) 

Album analysis

First, we’ll compare the how the various song features tracked by Spotify (danceability, energy, etc.) change across the four albums. My overall expectation is that the Fleet Foxes and Helplessness Blues albums are fairly similar, Crack-Up has a much more dissonance and lower valence, and Shore has higher valence.

ff_combined %>%
    pivot_longer(danceability:tempo) %>% 
    filter(!name %in% c("mode", "liveness")) %>% 
    ggplot(aes(x = value, y = album_name, fill = album_name)) +
        geom_density_ridges() +
        facet_wrap(~ name, scales = "free_x") + 
        theme_classic() +
        theme(legend.position = "none")

# This produces multiple larger charts (one for each track feature) instead

vars <- ff_combined %>%
    select(danceability:tempo) %>%
    colnames()

map(vars, ~ ff_combined %>%
    mutate(album_name = factor(album_name, levels = album_order)) %>%
    ggplot(aes(x = .x, y = album_name, fill = album_name)) +
        geom_density_ridges() +
        theme_classic() +
        theme(legend.position = "none") +
        labs(x = NULL, y = "Album"))

Some overall takeaways:

• Not surprised that Crack-Up has a uni-modal distribution of loud songs. Shore has a more uniform distribution of most song features, but especially loudness.
• Crack-Up is also distinctive in its distribution of keys, which has a much more concentrated distribution than the other albums.
• As expected, Crack-Up also sticks out in its valence distribution, with a uni-modal, low distribution. Shore is bi-modal, with concentrations of happier and sadder-sounding songs.
• Most songs’ tempos are a little slower on Shore than the previous two albums, though there is a second peak of higher-tempo songs. The first album, Fleet Foxes, stands out in this regard.

Tracks

The code below analyzes individual tracks by various track features.

ff_combined %>%
    arrange(desc(valence)) %>%
    #slice_head(n = 20) %>%
    mutate(track_name = factor(track_name, levels = track_name),
           track_name = fct_rev(track_name)) %>%
    ggplot(aes(y = track_name, x = valence, color = album_name)) +
    geom_segment(aes(x = 0, xend = valence, y = track_name, yend = track_name)) +
    geom_point(size = 3, alpha = 0.7) +
    theme_light() +
    labs(y = NULL, x = "Valence") + 
    theme(panel.grid.major.y = element_blank(),
          panel.border = element_blank(),
          axis.ticks.y = element_blank(), 
          text = element_text(size=9))

ff_combined %>%
    arrange(desc(danceability)) %>%
    #slice_head(n = 20) %>%
    mutate(track_name = factor(track_name, levels = track_name),
           track_name = fct_rev(track_name)) %>%
    ggplot(aes(y = track_name, x = danceability, color = album_name)) +
    geom_segment(aes(x = 0, xend = danceability, y = track_name, yend = track_name)) +
    geom_point(size = 3, alpha = 0.7) +
    theme_light() +
    labs(y = NULL, x = "danceability") + 
    theme(panel.grid.major.y = element_blank(),
          panel.border = element_blank(),
          axis.ticks.y = element_blank(), 
          text = element_text(size=9))

ff_combined %>%
    arrange(desc(energy)) %>%
    #slice_head(n = 20) %>%
    mutate(track_name = factor(track_name, levels = track_name),
           track_name = fct_rev(track_name)) %>%
    ggplot(aes(y = track_name, x = energy, color = album_name)) +
    geom_segment(aes(x = 0, xend = energy, y = track_name, yend = track_name)) +
    geom_point(size = 3, alpha = 0.7) +
    theme_light() +
    labs(y = NULL, x = "energy") + 
    theme(panel.grid.major.y = element_blank(),
          panel.border = element_blank(),
          axis.ticks.y = element_blank(), 
          text = element_text(size=9))

ff_combined %>%
    ggplot(aes(x = energy, y = valence, label = track_name)) + 
    geom_point(color = "#E32636", alpha = 0.7, size = 2) + 
    ggrepel::geom_text_repel(size = 3, data = subset(ff_combined, energy > .55 | energy < .38 | valence > .5)) +
    theme_classic() 

• Battery Kinzie is by far the highest valence song. I’m also not surprised to see Young Man’s Game and Lorelai in the top 6. The Plains / Bitter Dancer and Fool’s Errand are fitting for the lowest valence tracks. And 3 of the bottom 6 valence songs are on Crack-Up.
• I wouldn’t necessarily say any Fleet Foxes song is really all that danceable…
• 5 of the top 6 (and 7 of 9) highest energy songs are all on Shore!

Tracks and Sentiment Analysis

Next we can bring in some sentiments from the nrc data to compare whether valence aligns with positive vs. negative lyrics on individual tracks. Do some songs sound happy but have negative lyrics, or vice versa?

ff_sentiment <- ff_combined %>% 
    unnest_tokens(word, line) %>%
    select(album_name, track_name, word) %>%
    anti_join(stop_words) %>%
    inner_join(get_sentiments("nrc")) %>%
    distinct(word, track_name, .keep_all = TRUE) %>%
    mutate(sentiment = if_else(sentiment %in% c("anger", "fear", "negative", 
                                                "disgust", "sadness"), 
                               "negative", "positive")) %>% 
    count(track_name, sentiment) %>%
    group_by(track_name) %>%
    mutate(prop = round(n/sum(n), 2)) %>%
    filter(sentiment == "negative") %>%
    left_join(ff_combined %>% select(track_name, valence))

## Joining, by = "word"
## Joining, by = "word"

## Joining, by = "track_name"

ff_sentiment %>%
    ggplot(aes(prop, valence, label = track_name)) + 
    geom_point(color = "#E32636", alpha = 0.7, size = 2) + 
    ggrepel::geom_text_repel(size = 3, data = subset(ff_sentiment, valence > .25 | prop > .6 | prop < .4)) +
    theme_classic() +
    labs(x = "% Negative Emotions", y = "Valence")

Some of the sentiment analysis is tough because there aren’t a ton of words per song that have a sentiment in the nrc lexicon. However, it is interesting that many of the top valence songs, like Battery Kinzie, White Winter Hymnal, Lorelai, and Can I Believe you all have 50% or less positive emotions. That’s not surprising for Can I Believe You, which sounds upbeat but is about relationship trust issues.

LDA

Finally, we can do some LDA for unsupervised classification of songs into 5 topics.

ff_dtm <- ff_combined %>%
    rename(text = line) %>% 
    unnest_tokens(word, text) %>%
    anti_join(stop_words, by = "word") %>%
    count(track_name, word, sort = TRUE) %>%
    cast_dtm(track_name, word, n)

ff_lda <- LDA(ff_dtm, k = 5, control = list(seed = 123))



ff_topics <- ff_lda %>%
    tidy(matrix = "beta")


ff_top_terms <- ff_topics %>%
    group_by(topic) %>%
    top_n(5, abs(beta)) %>%
    ungroup() %>%
    arrange(topic, desc(beta))


ff_top_terms %>%
    mutate(term = reorder_within(term, beta, topic)) %>%
    ggplot(aes(beta, term, fill = factor(topic))) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~ topic, scales = "free") +
    scale_y_reordered()

So, our LDA algorithm found the following topics:

1. Songs with lyrics about the morning, with home-y related words (lie, home, apples).
2. Lyrics dominated by time, as well as walking with the devil.
3. Nature-dominated imagery – the ocean, rising, light and dark.
4. Another morning/sleep-focused topic, that seems to contrast with night-time and sleep-focused imagery (night, home, light, dream).
5. Water and memory-focused focused lyrics.

Seeing these 5 topics, it’s clear that nature, memory, light and darkness all factor heavily across all Fleet Foxes lyrics.

Finally, we can classify each song into one of the above topics.

ff_lda %>%
    tidy(matrix = "gamma") %>%
    group_by(document) %>%
    top_n(n = 1, wt = gamma) %>%
    arrange(topic) %>%
    kbl(booktabs = TRUE) %>%
    kable_styling(bootstrap_options = "striped") %>%
    kable_paper()