By: Alex Pirsos, 3/27/2020

Question

In the mid and late 2000’s, many popular Disney Channel stars left their acting careers to pursue music. However, it was apparent that the maturity of lyrics in their songs after Disney was starkly different than those during their childhood careers. This project aims to analyze the difference in lyric sentiment of popular Disney stars turned musicians while on the network and after.

Hypothesis

I predict that the sentiment of lyrics while on Disney Channel will be significantly more positive than the sentiment of lyrics after. I believe this will be apparent both in the types of lyrics used and the overall number of positive verse negative words.

Required R Packages

To complete this project I used the following packages: tidyverse, tidytext, genius, wordcloud2, and devtools.

r= getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
library(tidyverse)
library(tidytext)
library(genius)
library(wordcloud2)
library(devtools)
knitr::opts_chunk$set(echo = FALSE)

Set Up

To start this project I used an article that ranked the top 10 Disney Channel stars turned musicians. Of this sample, I had to disregard many artists because while they sang after appearing on Disney Channel, they did not release their own music before and during their Disney careers. This group included Vanessa Hudgens, Zendaya, Selena Gomez, Christina Aguilera, Justin Timberlake, and Britney Spears. In turn it left a group including Miley Cyrus, Demi Lovato, Hilary Duff, and The Jonas Brothers.

From there I wanted to analyze the sentiment of each of their albums before and after Disney Channel. I decided to also create a third category because many of these celebrities released music as a Disney Channel star (ex: Miley Cyrus as Hannah Montana). The following table shows which albums I analyzed for each artist.

Artist Solo Albums During Disney Career Albums as a Disney Channel Star Albums After Disney Career
Miley Cyrus Meet Miley Cyrus, Breakout, Can’t Be Tamed Hannah Montana, Hannah Montana 2, Hannah Montana: The Movie, Hannah Montana 3, Hannah Montana Forever Bangerz, Miley Cyrus & Her Dead Petz, Younger Now
The Jonas Brothers It’s About Time, Jonas Brothers, A little Bit Longer, Lies, Vines and Trying Times Jonas L.A., Camp Rock (Music From The Disney Channel Original Movie), Camp Rock 2: The Final Jam (Soundtrack From The Motion Picture Happiness Begins
Hilary Duff Santa Claus Lane, Metamorphosis, Hilary Duff Lizzie McGuire: Songs From The Hit TV Series, The Lizzie McGuire Movie Dignity, Breathe In. Breathe Out.
Demi Lovato Don’t Forget, Here We Go Again, Unbroken Camp Rock (Music From The Disney Channel Original Movie), Camp Rock 2: The Final Jam (Soundtrack From The Motion Picture), Sonny With A Chance Soundtrack Demi, Confident, Tell Me You Love Me

Note that I did not include The Jonas Brothers’ solo albums during the band’s breakup because they did not have solo careers during their time on Disney Channel. This also does not include any single releases outside of their albums or one-time partnership songs with other artists. The purpose is to focus on the sentiment of albums.

Method

For each artist I aggregated their albums grouping based on the three categories shown above. Before refers to solo albums released during their Disney career, During refers to albums released while portraying a Disney character, and After refers to albums released after leaving Disney Channel.

I then retrieved all of the lyrics from each album, unnested each line, and removed the stop words. Next, I used the sentiment analysis Afinn to plot the top 20 most frequent words used in the group of albums while showing the sentiment of those words. The following is the code for Miley Cyrus after Disney Channel. The code was then copied for each section of each artist.

Miley Cyrus

miley_albums_before <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Meet Miley Cyrus",
  "Miley Cyrus", "Breakout",
  "Miley Cyrus", "Can't Be Tamed"
)

miley_lyrics_before <- miley_albums_before %>% 
  add_genius(artist, title, type = "album")  

miley_sentiment_before <- miley_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

miley_sentiment_before <- miley_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

miley_sentiment_before %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Miley Cyrus Sentiment During Disney Channel")

miley_albums_during <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Hannah Montana",
  "Miley Cyrus", "Hannah Montana 2",
  "Miley Cyrus", "Hannah Montana: The Movie",
  "Miley Cyrus", "Hannah Montana 3",
  "Miley Cyrus", "Hannah Montana Forever"
)

miley_lyrics_during <- miley_albums_during %>% 
  add_genius(artist, title, type = "album")  

miley_sentiment_during <- miley_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

miley_sentiment_during <- miley_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

miley_sentiment_during %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Miley Cyrus Sentiment as a Disney Channel Character")

miley_albums_after <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Bangerz",
  "Miley Cyrus", "Miley Cyrus & Her Dead Petz",
  "Miley Cyrus", "Younger Now"
)

miley_lyrics_after <- miley_albums_after %>% 
  add_genius(artist, title, type = "album")  

miley_sentiment <- miley_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

miley_sentiment <- miley_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

miley_sentiment %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Miley Cyrus Sentiment After Disney Channel")

When comparing Miley Cyrus’ 3 graphs many interesting elements become apparent. For simplicity I will refer to the graphs in order of appearance as 1, 2, and 3.

Graph 1 and graph 2 both had value ranges of 6, whereas graph 3 had a range of over 7.5. This shows that after Disney Cyrus’ lyrics diversified and the sentiment of her lyrics became more polarizing and negative leaning. This can be seen by the obvious use of curse words.

All three graphs highlight the words yeah, love, and crazy, but the number of occurrences shifts drastically. In graph 1 the maximum number of occurrences per word is a little over 25 whereas in graphs 2 and 3 they are over 150. Perhaps during Cyrus’ youth, she explored more lyrics as she tried to define her image, thus leading to a greater variety of words, each occurring less. After and on Disney Cyrus had a very clear, defined image so maybe that is why certain words are used drastically more than others.

Overall, Cyrus does prove my hypothesis that after Disney Channel she sang more words that have a negative sentiment.

The Jonas Brothers

jb_albums_before <- tribble(
  ~ artist, ~ title,
  "Jonas Brothers", "It's About Time",
  "Jonas Brothers", "Jonas Brother",
  "Jonas Brothers", "A Little Bit Longer",
  "Jonas Brothers", "Lies, Vines and Trying Times"
)

jb_lyrics_before <- jb_albums_before %>% 
  add_genius(artist, title, type = "album")  

jb_sentiment_before <- jb_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

jb_sentiment_before <- jb_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

jb_sentiment_before %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Jonas Brothers Sentiment During Disney Channel")

jb_albums_during <- tribble(
  ~ artist, ~ title,
  "Jonas Brothers", "Jonas L.A.",
  "Cast Of Camp Rock ", "Camp Rock (Music From The Disney Channel Original Movie)",
  "Cast of Camp Rock 2: The Final Jam", "Camp Rock 2: The Final Jam (Soundtrack From The Motion Picture)"
)

jb_lyrics_during <- jb_albums_during %>% 
  add_genius(artist, title, type = "album")  

jb_sentiment_during <- jb_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

jb_sentiment_during <- jb_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

jb_sentiment_during %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics")  +
  ggtitle("Jonas Brothers Sentiment as Disney Channel Characters")

jb_albums_after <- tribble(
  ~ artist, ~ title,
  "Jonas Brothers", "Happiness Begins"
)

jb_lyrics_after <- jb_albums_after %>% 
  add_genius(artist, title, type = "album") 

jb_sentiment <- jb_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

jb_sentiment <- jb_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

jb_sentiment %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics")  +
  ggtitle("Jonas Brothers Sentiment After Disney Channel")

For The Jonas Brothers, graph 1 shows a range of 6 with the fewest occurrences maxing out over 25. There is a positive leaning sentiment based on this scale. Graph 2 visually looks more negative however it is misleading because it ranges from -2 to 4 which is actually higher than graph 1. Similarly to Cyrus, we see The Jonas Brothers with a more positive sentiment during their Disney Characters careers.

Where The Jonas Brothers differ from Cyrus is graph 3. Here we see the same value range as graph 2 with arguably a more positive leaning sentiment. With that said, graph 3 does only look at one album which is titled Happiness Begins. The only words to repeat across all three graphs are yeah, love, and feeling. The Jonas Brothers disprove the original hypothesis that the sentiment will be more positive during their Disney Channel careers.

Hilary Duff

duff_albums_before <- tribble(
  ~ artist, ~ title,
  "Hilary Duff", "Santa Claus Lane",
  "Hilary Duff", "Metamorphosis",
  "Hilary Duff", "Hilary Duff"
)

duff_lyrics_before <- duff_albums_before %>% 
  add_genius(artist, title, type = "album")  

duff_sentiment_before <- duff_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

duff_sentiment_before <- duff_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

duff_sentiment_before %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Hilary Duff Sentiment During Disney Channel")

duff_albums_during <- tribble(
  ~ artist, ~ title,
  "Walt Disney Records", "Lizzie McGuire: Songs From The Hit TV Series",
  "Various Artists", "The Lizzie McGuire Movie"
)

duff_lyrics_during <- duff_albums_during %>% 
  add_genius(artist, title, type = "album")  

duff_sentiment_during <- duff_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

duff_sentiment_during <- duff_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

duff_sentiment_during %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Hilary Duff Sentiment as a Disney Channel Character")

duff_albums_after <- tribble(
  ~ artist, ~ title,
  "Hilary Duff", "Dignity",
  "Hilary Duff", "Breathe In. Breathe Out."
)

duff_lyrics_after <- duff_albums_after %>% 
  add_genius(artist, title, type = "album")  

duff_sentiment <- duff_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

duff_sentiment <- duff_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

duff_sentiment %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics")  +
  ggtitle("Hilary Duff Sentiment After Disney Channel")

For Hilary Duff in graph 1 there was more variety of lyrics as the maximum number of occurrences was only slightly over 30. The range in value was from -2 to 4 with an even mix of words falling into each category. In graph 2 we see a smaller range that is actually slightly less positive than the sentiment of Duff’s lyrics as a solo artist during this time with more occurrences of said words. Finally, in graph 3, we revert to a slightly more negative range but with many more occurrences. The only words to repeat in all three graphs are love, dream(s), kiss, and yeah.

Duff both proves and disproves the original hypothesis. She proves it because after Disney Channel her lyrics do have a more negative sentiment. However, she also disproves it because her lyrics as a Disney Channel Character were not the most positive. This means she produced more positive content without the direct influence of Disney.

Demi Lovato

demi_albums_before <- tribble(
  ~ artist, ~ title,
  "Demi Lovato", "Don't Forget",
  "Demi Lovato", "Here We Go Again",
  "Demi Lovato", "Unbroken"
)

demi_lyrics_before <- demi_albums_before %>% 
  add_genius(artist, title, type = "album")  

demi_sentiment_before <- demi_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

demi_sentiment_before <- demi_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

demi_sentiment_before %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics") +
  ggtitle("Demi Lovato Sentiment During Disney Channel")

demi_albums_during <- tribble(
  ~ artist, ~ title,
  "Cast Of Camp Rock ", "Camp Rock (Music From The Disney Channel Original Movie)",
  "Cast of Camp Rock 2: The Final Jam", "Camp Rock 2: The Final Jam (Soundtrack From The Motion Picture)",
  "Various Artists", "Sonny With A Chance Soundtrack"
)

demi_lyrics_during <- demi_albums_during %>% 
  add_genius(artist, title, type = "album")  

demi_sentiment_during <- demi_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

demi_sentiment_during <- demi_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

demi_sentiment_during %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics")  +
  ggtitle("Demi Lovato Sentiment as a Disney Channel Character")

demi_albums_after <- tribble(
  ~ artist, ~ title,
  "Demi Lovato", "Demi",
  "Demi Lovato", "Confident",
  "Demi Lovato", "Tell Me You Love Me"
)

demi_lyrics_after <- demi_albums_after %>% 
  add_genius(artist, title, type = "album")  

demi_sentiment <- demi_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  head(12) %>% 
  ggplot(aes(word, n)) + geom_col ()

demi_sentiment <- demi_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  inner_join(get_sentiments("afinn"))

demi_sentiment %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n, fill=value)) + 
  geom_col() +
  coord_flip() +
  ylab("Number of Occurrences") +
  xlab("Lyrics")  +
  ggtitle("Demi Lovato Sentiment After Disney Channel")

Finally, Demi Lovato curates similar results to Cyrus. Starting with her solo albums, graph 1 shows the smallest range of 5 with occurrences over 50. Graph 2 expands the range slightly more positive with a range of 6 and similar 60 occurrences. Where we see the big leap is after her Disney Channel career. Now in graph 3, we see a range of 7.5 from -5 to 2.5 with the top two words occurring over 150 times. Also like Cyrus, we see the introduction of explicit words showing a break from the influence of Disney of her public image and sound. The words to repeat in all three graphs are yeah, love, hard, and leave.

Word Cloud Group Analysis

To explore each section of the artists’ lyrics more in depth, I created three word clouds. Each represents the most used words by the four artists’ albums together as solo artists during their Disney careers, as Disney Characters, and after Disney. This provides another look at the sentiment of words used but this time in aggregate. The word clouds showcase words that had a minimum of 3 occurrences. Below each is a frequency table displaying the top 10 words from each cloud and how many times they were used.

Word Cloud of Lyrics Used During Disney Channel

all_albums_before <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Meet Miley Cyrus",
  "Miley Cyrus", "Breakout",
  "Miley Cyrus", "Can't Be Tamed",
  "Jonas Brothers", "It's About Time",
  "Jonas Brothers", "Jonas Brother",
  "Jonas Brothers", "A Little Bit Longer",
  "Jonas Brothers", "Lies, Vines and Trying Times",
  "Hilary Duff", "Santa Claus Lane",
  "Hilary Duff", "Metamorphosis",
  "Hilary Duff", "Hilary Duff",
  "Demi Lovato", "Don't Forget",
  "Demi Lovato", "Here We Go Again",
  "Demi Lovato", "Unbroken"
)

all_lyrics_before <- all_albums_before %>% 
  add_genius(artist, title, type = "album") 

all_sentiment_before <- all_lyrics_before %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE)

all_sentiment_before %>%
  head(691) -> beforeWordcloud1
wordcloud2(beforeWordcloud1)

Frequency of Top 10 Words for Lyrics Used During Disney Channel

Rank Word Number of Times Used
1 la 203
2 wanna 144
3 gonna 143
4 love 137
5 time 128
6 heart 112
7 yeah 108
8 christmas 88
9 girl 84
10 feel 82

Word Cloud of Lyrics Used As Disney Channel Characters

all_albums_during <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Hannah Montana",
  "Miley Cyrus", "Hannah Montana 2",
  "Miley Cyrus", "Hannah Montana: The Movie",
  "Miley Cyrus", "Hannah Montana 3",
  "Miley Cyrus", "Hannah Montana Forever",
  "Jonas Brothers", "Jonas L.A.",
  "Cast Of Camp Rock ", "Camp Rock (Music From The Disney Channel Original Movie)",
  "Cast of Camp Rock 2: The Final Jam", "Camp Rock 2: The Final Jam (Soundtrack From The Motion Picture)",
  "Various Artists", "Sonny With A Chance Soundtrack",
  "Walt Disney Records", "Lizzie McGuire: Songs From The Hit TV Series",
  "Various Artists", "The Lizzie McGuire Movie"
)

all_lyrics_during <- all_albums_during %>% 
  add_genius(artist, title, type = "album") 

all_sentiment_during <- all_lyrics_during %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE)

all_sentiment_during %>%
  head(795) -> duringWordcloud1
wordcloud2(duringWordcloud1)

Frequency of Top 10 Words for Lyrics Used As Disney Channel Characters

Rank Word Number of Times Used
1 yeah 333
2 gonna 263
3 na 207
4 time 175
5 girl 172
6 hey 161
7 love 140
8 start 131
9 baby 119
10 rock 118

Word Cloud of Lyrics Used After Disney Channel

all_albums_after <- tribble(
  ~ artist, ~ title,
  "Miley Cyrus", "Bangerz",
  "Miley Cyrus", "Miley Cyrus & Her Dead Petz",
  "Miley Cyrus", "Younger Now",
  "Jonas Brothers", "Happiness Begins",
  "Hilary Duff", "Dignity",
  "Hilary Duff", "Breathe In. Breathe Out.",
  "Demi Lovato", "Demi",
  "Demi Lovato", "Confident",
  "Demi Lovato", "Tell Me You Love Me"
)

all_lyrics_after <- all_albums_after %>% 
  add_genius(artist, title, type = "album") 

all_sentiment <- all_lyrics_after %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE)

all_sentiment %>%
  head(950) -> afterWordcloud1
wordcloud2(afterWordcloud1)

Frequency of Top 10 Words for Lyrics Used After Disney Channel

Rank Word Number of Times Used
1 love 487
2 yeah 395
3 na 208
4 la 195
5 time 179
6 baby 173
7 ooh 155
8 feel 118
9 heart 116
10 wanna 113

At first glance, the word clouds appear relatively similar. They all showcase many filler words such as la, na, and yeah as well as generic words such as love and time. Where we really notice a difference is in the less apparent words. As mentioned before, Word Cloud 3 similarly to graph 3 for many artists feature explicit and sexualized words. This makes sense as the artists want to sing about more mature topics. It’s also interesting to note the number of words in each cloud. Each graphic only displays words that were sung at least 3 times. Word cloud 1 contains 950 words, word cloud 2 contains 691 words, and word cloud 3 contains 795 words. This perhaps correlates to my prediction before that as the artists were trying to explore their sound and find their voice earlier in their music careers they played around with a wider variety of words, thus the largest amount in word cloud 1. Conversely, Disney has a very streamlined image which could lead to the least amount of variety of words in word cloud 2. We can also see this through the frequency of words. The top word in Word Cloud 3 was used 487 times where as the top word in Word Cloud 1 was only used 203 times. Overall, the word clouds again show that while there are slight differences in lyric sentiment from different points in time for the artists, it is not as drastic as my hypothesis originally alluded to and is more apparent in the frequency of words.

Assumptions

It is important to note that all songs on each album were analyzed regardless of the writer or singer. The majority of songs were sung by the artist who released it, however there are some exceptions For example, the Camp Rock album features some singers outside of Demi Lovato and The Jonas Brothers. These songs were included because the album as a whole branded and influenced the artists images, even if they did not directly write, produce, or sing a number of songs.

Explanation

I thought there would be a stronger difference in sentiment especially when comparing the lyrics artists sang as Disney Channel stars and after. While they were seemingly more negative and explicit as predicted, the difference was not as drastic as originally thought. In addition, we see that Disney’s slight influence on lyrics only applied to some members of the sample. This analysis does not even cover the other 6 artists who only released solo music after starring on Disney, let alone other artists not considered on the top ten list. It makes sense that the network would want to streamline their corporate image and sound, and quite frankly the results show that they may have room to control even more of the music produced and advertised through their network’s platform given that it was not as harsh of a difference as current viewers likely assume.

Conclusion

In conclusion, while many of the artists do produce lyrics with a more negative sentiment after starring on Disney, there is not a significant difference between the sentiment and lyrics released on their albums before, during, and after Disney. From a consumer standpoint this is helpful to know when listening to artists lyrics that the sentiment will not change drastically as they mature with the exception of added expletives. For artists, this could be helpful to know that it will take a major change in the lyrics used to change the public perception of the artist and their music.