"Ode to Viceroy": Mac DeMarco's Influence on Interest in Viceroy Cigarettes

Mac DeMarco released his first album, 2, on October 16th, 2012. The fifth track is called “Ode to Viceroy,” which is a song about the Viceroy brand cigarette. He’s gained in popularity with his subsequent releaes, Salad Days and This Old Dog, and his affection for cigarettes had turned into somewhat of a meme.

What has the effect of “Ode to Viceroy” been on the popularity of the Viceroy brand itself?

I looked at this question by analyzing frequency of Google searches (accessed via the gtrendsR R package) using the CausalImpact R package.

I pulled the frequency of Google searches for a number of cigarette brands. I went looking around Wikipedia and found that Viceroy is owned by the R. J. Reynolds Tobacco Company. This sentence was on that company’s Wikipedia page: “Brands still manufactured but no longer receiving significant marketing support include Barclay, Belair, Capri, Carlton, GPC, Lucky Strike, Misty, Monarch, More, Now, Tareyton, Vantage, and Viceroy.”

I took each of these brand names and attached “cigarettes” to the end of the query (e.g., “GPC cigarettes”). I didn’t use “More,” due to the majority of queries for “More cigarettes” was probably not in reference to the More brand. I pulled monthly search numbers for each of these brands from Google Trends. I set the date range to be four years before (October 16th, 2008) and after (October 16th, 2016) 2 came out.

The CausalImpact package employs Bayesian structural time-series models in a counterfactual framework to estimate the effect of an intervention. In this case, the “intervention” is Mac DeMarco releasing 2. Basically, the model asks the question: “What would have the data looked like if no intervention had taken place?” In the present case, the model uses information I gave it from Google Trends about the handful of cigarette brands, and then it estimates search trends for Viceroy if 2 had never been released. It then compares this “synthetic” data against what we actually observed. The difference is the estimated "causal impact" of Mac DeMarco on the popularity of Viceroy cigarettes. (I highly suggest reading the paper, written by some folks at Google, introducing this method.)

When doing these analyses, we assume two crucial things: First, that none of the other brands were affected by Mac DeMarco releasing 2; and Second, that the relationships between the other brands and Viceroy remained the same after the album’s release as before the release.

Google Trends norms their data and scales it in a way that isn’t readily interpretable. For any trend that you retrieve, the highest amount of searches is set to the value of 100. Every other amount is scaled to that: If you observe a 50 for one month, that means it is 50% the value of the number of searches observed at the max in that time period. Keep this in mind when looking at the results.

You can see the trend below. The black line is what we actually observed for the amount of Google queries for “Viceroy cigarettes.” The dashed vertical line represents when Mac released 2. The dashed blue line is what we estimate would have been the trend if Mac hadn’t ever released his album, and the lighter blue area above and below this line represents our uncertainty in this dashed blue line. Specifically, there is 95% probability that the dashed blue line is somewhere in that light blue range.

We can see that what we actually observed goes outside of this blue range about a year after Mac released his album. According to the model, there is greater than a 99.97% probability that Mac DeMarco had a positive effect on people Googling “Viceroy cigarettes”. On average, the difference between what we actually observed and the estimated trend if Mac didn’t release 2 was 31, and there’s a 95% probability that this difference is between 27 and 35. This number is a little hard to interpret (given how the data are normed and scaled), but one could say that the estimated causal impact—for the average month in the four years after Mac's first album—is about 31% of whatever the highest observed number of monthly search queries were for “Viceroy cigarettes” in this same time.

But how do we know this is due to Mac? We can't ever be 100% sure, but if you look at Google Trends for "Viceroy cigarettes" in the four years after he released his album, the top "related query" is "Mac DeMarco."

In one song, Mac DeMarco was able to get people more interested in Viceroy cigarettes. I’m interested in how this affected sales—I’d bet there is at least some relationship between Google searches and sales.

Code for all data collection and analyses is available on my GitHub.

Sentiment Analysis of Kanye West's Discography


Last time, I did some basic frequency analyses of Kanye West lyrics. I've been using the tidytext package at work a lot recently, and I thought I would apply some of the package's sentiment dictionaries to the Kanye West corpus I have already made (discussed here). The corpus is not big enough to do the analyses by song or by a very specific emotion (such as joy), so I will stick to tracking positive and negative sentiment of Kanye's lyrical content over the course of his career.


For each song, I removed duplicate words (for reasons like so that the song “Amazing” doesn't have an undue influence on the analyses, given that he says “amazing"—a positive word—about 50 times). I allowed duplicate words from the same album, but not from the same song.

The tidytext package includes three different sentiment analysis dictionaries. One of the dictionaries assigns each word a score from -5 (very negative) to +5 (very positive). Using this dictionary, I simply took the average score for the album:

afinn <- kanye %>% # starting with full data set
  unnest_tokens(word, lyrics) %>% # what was long string of text, now becomes one word per row
  inner_join(get_sentiments("afinn"), by="word") %>% # joining sentiments with these words
  group_by(song) %>%  # grouping dataset by song
  subset(!duplicated(word)) %>% # dropping duplicates (by song, since we are grouped by song)
  ungroup() %>% # ungrouping real quick...
  group_by(album) %>% # and now grouping by album
  mutate(sentiment=mean(score)) %>%  # getting mean by album
  slice(1) %>% # taking one row per every album
  ungroup() %>% # ungrouping
  subset(select=c(album,sentiment)) # only including the album and sentiment columns

The other two dictionaries tag each word as negative or positive. For these, I tallied up how many positive and negative words were being used per album and subtracted the negative count from the positive count:

bing <- kanye %>% # taking data set
  unnest_tokens(word, lyrics) %>% # making long list of lyrics into one word per row
  inner_join(get_sentiments("bing"), by="word") %>% # joining words with sentiment
  group_by(song) %>% # grouping by song
  subset(!duplicated(word)) %>% # getting rid of duplicated words (within each song)
  ungroup() %>% # ungrouping
  count(album, sentiment) %>% # counting up negative and positive sentiments per album
  spread(sentiment, n, fill=0) %>% # putting negative and positive into different columns
  mutate(sentiment=positive-negative) # subtracting negative from positive

# all of this is same as above, but with a different dictionary
nrc <- kanye %>% 
  unnest_tokens(word, lyrics) %>% 
  inner_join(get_sentiments("nrc"), by="word") %>%
  group_by(song) %>% 
  subset(!duplicated(word)) %>% 
  ungroup() %>% 
  count(album, sentiment) %>%
  spread(sentiment, n, fill=0) %>% 

I then merged all these data frames together, standardized the sentiment scores, and averaged the three z-scores together to get an overall sentiment rating for each album:

colnames(afinn)[2] <- "sentiment_afinn" # renaming column will make it easier upon joining

bing <- ungroup(subset(bing, select=c(album,sentiment))) # getting rid of unnecessary columns
colnames(bing)[2] <- "sentiment_bing"

nrc <- ungroup(subset(nrc, select=c(album,sentiment)))
colnames(nrc)[2] <- "sentiment_nrc"

suppressMessages(library(plyr)) # getting plyr temporarily because I like join_all()
album_sent <- join_all(list(afinn, bing, nrc), by="album") # joining all three datasets by album
suppressMessages(detach("package:plyr")) # getting rid of plyr, because it gets in the way of dplyr

# creating composite sentiment score:
album_sent$sent <- (scale(album_sent$sentiment_afinn) + 
                    scale(album_sent$sentiment_bing) + 
album_sent <- album_sent[-7,c(1,5)] # subsetting data to not include watch throne and only include composite
# reordering the albums in chronological order
album_sent$album <- factor(album_sent$album, levels=levels(album_sent$album)[c(2,4,3,1,5,7,8,6)])


And then I plotted the sentiment scores by album, in chronological order. Higher scores represent more positive lyrics:

ggplot(album_sent, aes(x=album, y=sent))+
  geom_line(group=1, linetype="longdash", size=.8, alpha=.5)+
  labs(x="Album", y="Sentiment")+
  scale_x_discrete(labels=c("The College Dropout", "Late Registration", "Graduation",
                            "808s & Heartbreak", "MBDTF", "Yeezus", "The Life of Pablo"))+
  theme(axis.text.x=element_text(angle=45, hjust=1),
        text = element_text(size=12))

plot of chunk unnamed-chunk-32

Looks like a drop in happiness after his mother passed as well as after starting to date Kim Kardashian…

This plot jibes with what we know: 808s is the "sad” album. Graduation—with all the talk about achievement, being strong, making hit records, and having a big ego—peaks at the album with the most positive words. This plot seems to lend some validity to the method of analyzing sentiments using all three dictionaries from the tidytext package.

Some issues with this “bag-of-words” approach, however, was that it was prone to error in the finer level of analysis, like song. “Street Lights,” perhaps one of Kanye's saddest songs, came out with one of the most positive scores. Why? It was reading words like “fair” as positive, neglecting to realize that a negation word (i.e., “not”) always preceded it. One could get around this with maybe n-grams or natural language processing.

Nevertheless, there's the trajectory of Kanye's lyrical positivity over time!

Text Mining Kanye's Vocabulary


I have been teaching myself how to do some text mining in R, and I thought a fun corpus to look at would be Kanye West lyrics. I'm still learning sentiment analysis, so this post will focus on basic frequencies for words in Kanye West songs. I'm going to look at the lyrics from Kanye's discrography (his solo albums and Watch the Throne) to see (a) the words he uses most and (b) which albums have the biggest vocabularies.

Making the corpus

I wanted to do scrape a website like Genius.com to automate the data collection, but sadly there were too many issues: How should I handle inconsistent formatting? What about when it says “Verse 1”? How do I edit out lyrics that Kanye didn't rap or sing? I just did a lot of copy-and-pasting into .txt files.

I edited down the files so that only verses by Kanye were included—I excluded features. I also took out the lyrics that were samples, choruses sung by other people, all spoken word introductions and outroductions, and skits. As is typical of text mining, I took out “stop words,” which are basically just boring words that I don't want to muck up my analyses (e.g., the, and, or). I also made it so that words like “change,” “changing,” and “changed” all counted as one word.

Like I said above, I included all of Kanye West's solo albums. My friends and I generally consider Watch the Throne to be primarily a Kanye album (save for American Gangster, Did Jay-Z ever recover from collaborating with Linkin Park in 2004?), so I included lyrics by Kanye West on this record.

Frequently used words

The first thing I did was look at the most frequently used words Kanye uses throughout his discography. He uses 3,294 unique words; he uses 1,741 of these only once, 479 twice, and 242 three times. Here is a figure showing all the words he has used 99 or more times:

plot of chunk unnamed-chunk-69

Kanye West coined the term “hashtag rap,” which, in Kanye's words, involves taking “the 'like' or 'as' out of the metaphor” (think: “Here's another hit—Barry Bonds” instead of “Here's another hit, like Barry Bonds”). Despite this, “like” is the word Kanye uses most throughout his discography. “Know,” “get,” “now,” just,“ "don't,” and “got” follow after. To my eye, this collection of words seems like a lot of words that deal with achieving or demanding things.

Which album has the biggest vocabulary?

The next thing I did was create a “uniqueness score” for each album. I started by calculating a score for each album: the number of unique words in a song divided by the number of words total in a song. For example, if a song had 25 unique words and 50 total words in the song, it would have a score of 0.5. The phrase “Hello, hello, everybody” has a score of 0.66, as there are two unique words (“hello” and “everybody”), and there are a total of three words in the phrase.

Then I simply took these songs and averaged them across album. So if an album was four songs and these songs had scores of .20, .40, .60, and .80, then the album's “uniqueness score” would be 0.50. Here is a plot of the albums' “uniqueness scores” (i.e., average vocabulary size), in chronological order:

plot of chunk unnamed-chunk-71

He's been remarkably consistent throughout his discography. The only notable difference is 808s and Heartbreak, which makes sense—he does not rap the lyrics, but sings them (mostly). What my next post on this dataset will include is the content of his vocabulary: How has the content of his lyrics changed over time?