R Notebook

library(genius)                         # This package gets lyrics
library(tidyverse)
library(tidytext)
library(wordcloud2)

1. Find the album and get the lyrics, and unnest them.

fearless <- genius_album(artist = "taylor swift", album = "fearless")

Joining, by = c("album_name", "track_n", "track_url")

fearless_words <- fearless %>%
  unnest_tokens(word, lyric) %>% 
  select(track_title, word)

fearless_words

Here is the unnested words from the album “fearless” by taylor swift.

2. Clean the lyrics by removing stopwords, and then create a table and word cloud with the words counts.

fearless_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort = T)

Joining, by = "word"

table of non-stop words.

fearless_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort = T)%>%
  top_n(100)%>%
  wordcloud2(size = .5)

Joining, by = "word"
Selecting by n

word cloud without stop words but with word count.

3. Do the sentiment analysis as above, and create graphs of the words that contribute most to each sentiment.

bing <- get_sentiments("bing")
bing

This is the sentient analysis.

fearless_words %>% 
  inner_join(bing) %>% 
  count(word, sentiment, sort = TRUE) %>% 
  filter(sentiment == "positive") %>%
  select(word, n) %>% 
  wordcloud2()

Joining, by = "word"

fearless_words %>% 
  inner_join(bing) %>% 
  count(word, sentiment, sort = TRUE) %>% 
  filter(sentiment == "negative") %>%
  select(word, n) %>% 
  wordcloud2()

Joining, by = "word"

NA

These are the graphs showing the words that contribute most to each sentiment.

4. Create bigrams of the lyrics, remove the stopwords, and create a table and word cloud of the most common bigrams.

fearless %>%
  unnest_tokens(bigram, lyric, token = "ngrams", n = 2) %>% 
  select(bigram) -> fearless_bigrams

fearless_bigrams %>% 
  separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  unite(bigram, word1, word2, sep = " ")

NA

Here is a bigram of the lyrics with the stop words removed.

fearless_bigrams %>% 
  separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  unite(bigram, word1, word2, sep = " ")%>%
  count(bigram, sort= T)

Here is a table showing the most common bigrams when the stop words are removed.

fearless_bigrams %>% 
  separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  unite(bigram, word1, word2, sep = " ")%>%
  count(bigram, sort= T)%>%

  filter(n > 1) %>% 
  wordcloud2(size = .5)

This is a word cloud of the most common bigrams.

5. Use the bigram method to find the most common words that come after words of your choice, like i/you or he/she.

first_word <- c("i", "you")                                  # these need to be lowercase

fearless_bigrams %>% 
  count(bigram, sort = T) %>% 
  separate(bigram, c("word1", "word2"), sep = " ") %>%       # separate the two words
  filter(word1 %in% first_word) %>%                          # find first words from our list
  count(word1, word2, wt = n, sort = TRUE)

NA

This is a table of the most common words that come after the words “i” and “you”.