Lyric and Sentiment Analysis of Music by One Direction vs. Individual Members
Maria Rossi
10/9/2020
Introduction and Background
One Direction was a pop boy-band consisting of five members that each auditioned indivudually for the British X-Factor in 2010. Harry Styles, Niall Horan, Zayn Malik, Liam Payne, and Louis Tomlinson were put together in the band during their time on the show, and while they did not win, the band went on to become a worldwide sensation. Over the course of the band’s career, they released five studio albums and went on four world tours. Zayn Malik left the group in 2015, creating his own independent album. The rest of the One Direction members ended up parting ways in 2016, each embarking on their own musical endeavors. Since then, all five have created and contributed to writing at least one studio album, each with varying degrees of success. With more independence and creative freedom, this begs the question: are the songs put out by each individual member more mature and negative in tone than the songs produced by the band as a whole?
Hypothesis
This analysis aims to look at the hypothesis that the members of One Direction created songs that were more mature and negative in terms of lyric sentiment as individual music artists than the songs produced by the band as a whole.
The Analysis
To begin, the required pacakges need to be loaded.
library(genius)
library(tidyverse)
library(tidytext)
library(wordcloud2)
library(ggthemes)
library(textdata)The Band as a Whole
As a band, One Direction released five studio albums. Using the Genius library, their music can be broken down into lyrics by album.
oned_albums<- tribble(
~artist,~title,
"One Direction", "Up All Night",
"One Direction", "Take me Home",
"One Direction", "Midnight Memories",
"One Direction", "FOUR",
"One Direction", "Made in the A.M.")
oned_lyrics<-oned_albums %>%
add_genius(artist,title,type="album")Next, I seperated the lyrics by line in order to get the words by themselves and also removed any of the stop words. Each word is then counted to give us a list of the most frequent words, which I have then placed into a word cloud. The top 10 words can be looked at more closely in the bar graph below. Since I want to look at the band’s discography as a whole, rather than break it down by album, I will look at the sentiment and word frequency across the five albums all together.
oned_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
count(word,sort=TRUE)->oned_words
wordcloud2(oned_words)oned_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in One Direction's Albums") + theme_solarized()+labs(y="Occurrences", x="Word")
You will notice I chose not to remove filler words such as “na” and “yeah”, which is because I think it encaptures the pop-ness of their music. These kinds of words are very popular in pop music, especially that of boy bands, and I think will make for an interesting comparison against the lyrics of the music by the individual members.
Based on the popular words across all five albums, it is clear that their music leans towards the typical boy-band content one would expect to cater towards their primarily young, teenage girl audience. The words “na” and “yeah” being the most prevalent goes to show that their music was the kind of catchy and easy-to-sing-along to songs that dominate the pop world. They also sang quite a lot about young love, with songs titled things like “What Makes you Beautiful”, “Summer Love”, “You and I”, and “Where do Broken Hearts Go”, among a slew of others. Therefore, it is unsurpising that the words “baby”, “heart”, love“, and”girl" make the list as being very popular lyrics in their songs. There is also a large presence of more generic words, such as night, time, and tonight, which are used in their songs in reference to going out, having fun, and being young.
To get a sense of how positive or negative the music by One Direction was, a sentiment analysis can be done using the “afinn” lexicon, which uses a scale from -5 to 5 to rank the overall sentiment of the words, with -5 being the most negative and 5 being the most positive.
oned_words%>%
inner_join(get_sentiments("afinn"))->oned_sentiment
oned_sentiment %>%
arrange(desc(-value))->oned_neg
oned_sentiment %>%
arrange(desc(value))->oned_pos
oned_neg %>%
head(10)->top10_neg
oned_pos %>%
head(10) -> top10_pos
top10_neg## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 lost 11 -3
## 2 bad 10 -3
## 3 die 6 -3
## 4 madly 6 -3
## 5 worry 5 -3
## 6 desperately 4 -3
## 7 hate 4 -3
## 8 worse 3 -3
## 9 fake 2 -3
## 10 frightening 2 -3top10_pos## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 win 2 4
## 2 amazing 1 4
## 3 fun 1 4
## 4 funny 1 4
## 5 love 181 3
## 6 loved 31 3
## 7 beautiful 19 3
## 8 perfect 15 3
## 9 luck 4 3
## 10 super 3 3mean(top10_neg$value)## [1] -3mean(top10_pos$value)## [1] 3.4Looking at their 10 most positive and negative words in descending order by afinn value, there are 258 occurrunces of the top 10 most positive words and 53 of the top 10 most negative words. The mean value of the top 10 positive words is 3.4 wheras the mean for the top 10 most negative words is -3.
These values tell us that there are more words associated with higher afinn values than there are words associated with lower values, meaning that there are more strongly positive words than negative ones. One Direction’s lyrics also reach a higher positive value, with 5 occurrences of words with a value of 4, compared to the negative side of the scale where the most negative words have a value of 3.
While these values are important, we may also want to look at the sentiment of the most prevalent words across all their albums. To do this, I took the top 10 words I found earlier and compared the sentiment of each.
oned_sentiment_graph<- oned_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
oned_sentiment_graph <-oned_words %>%
inner_join(get_sentiments("afinn"))
oned_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("One Direction Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
Since some words are not included in the afinn lexicon, they do not have a value attached to them and are thus removed from the list of words being analyzed. Based on this graph, we can see that the range of sentiment values of the top 10 words that afinn recognizes goes from -2 to 3. The top two words, “yeah” and “love”, are also some of the most positive words, though the words that follow in their popularity seem to decrease in their sentiment value to include more negative words such as “stop” and “leave”.
Now for the Individual Members
Zayn Malik
Zayn Malik, or more commonly known by his stage name ZAYN, was the first to leave the band in 2015 before their 2016 breakup. He released his first album, Mind of Mine, in early 2016. Malik took on a less poppy tone than that of his boy-band days and went with a more downbeat, R&B style. This style remained throughout the rest of his two studio albums, featuring prominent rappers and R&B artists on several tracks.
First, I gathered all three of Malik’s albums and broke them down into their lyrics.
zayn_albums<- tribble(
~artist,~title,
"ZAYN", "Mind of Mine",
"ZAYN", "Poems",
"ZAYN", "Icarus Falls")
zayn_lyrics<-zayn_albums %>%
add_genius(artist,title,type="album")As I did with the band as a whole, I next seperated each word indivually and removed the stop words. Again, I am interested in looking at all of his albums together rather than seperate, so I will not break it down by album.
With this, I can now create a word cloud of all of his lyrics as well as a visualization of the top 10 lyrics of his songs.
zayn_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
count(word,sort=TRUE)->zayn_words
wordcloud2(zayn_words)zayn_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in ZAYN's Albums") + theme_solarized()+labs(y="Occurrences", x="Word")
To remain consistent, I will not remove any filler words, such as “yeah” or “ya”, because I think these are good indicators of the maturity and kind of music being produced.
The most frequent words in Malik’s albums does not appear to differ that much from when he was a member of One Direction. Many words can be seen to overlap and there appears to be a similar theme of love and talking about a female interest. What is interesting, though, is that there is far less use of filler words. While “na” and “yeah” made the most frequent appearences in One Direction songs, Malik opts for less usage of these predominately pop sounds. There also looks to be more diversity in the words on Malik’s albums, in the sense that the word cloud shows words that are less generic than that of One Direction’s. We start to see some curse words and words with more serious connotations when compared to the typically light, and upbeat lyrics found in One Direction songs. This leads us into looking at the sentiment of Malik’s songs.
Again, the afinn lexicon will be used to look at how positive or negative his lyrics tend to be.
zayn_words%>%
inner_join(get_sentiments("afinn"))->zayn_sentiment
zayn_sentiment %>%
arrange(desc(-value))->zayn_neg
zayn_sentiment %>%
arrange(desc(value))->zayn_pos
zayn_neg %>%
head(10)->top10_neg_zayn
zayn_pos %>%
head(10) -> top10_pos_zayn
top10_neg_zayn## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 shit 12 -4
## 2 fuck 7 -4
## 3 fucked 5 -4
## 4 fucking 3 -4
## 5 piss 3 -4
## 6 damned 2 -4
## 7 hell 2 -4
## 8 damn 1 -4
## 9 lost 11 -3
## 10 losing 7 -3top10_pos_zayn## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 win 2 4
## 2 fun 1 4
## 3 miracle 1 4
## 4 wins 1 4
## 5 wow 1 4
## 6 love 149 3
## 7 paradise 14 3
## 8 beautiful 4 3
## 9 loved 3 3
## 10 pleasure 3 3mean(top10_neg_zayn$value)## [1] -3.8mean(top10_pos_zayn$value)## [1] 3.5Here we start to see more of a stark difference in the lyrics of Malik’s post-One Direction days. With 176 occurrences of the top 10 most positive words and a mean of 3.5, there is not much of a difference in the positive sentiment of Mailk’s music vs. that of One Direction. They both also reach a high value of 4, with “love” being the common most popular word. The negative words is where the difference comes in. With 53 occurrences of the 10 most negative words and a mean of -3.8, it would again appear to be similar. Yet, 8 out of 9 of these words are curse words, all with values of -4. None of these kinds of words exist in One Direction songs, indicating that Malik began putting out more mature content.
I also want to look at the sentiment of the most common words in his music, as I did with the band.
zayn_sentiment_graph<- zayn_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
zayn_sentiment_graph <-zayn_words %>%
inner_join(get_sentiments("afinn"))
zayn_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("ZAYN Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
Again, this only includes words that the afinn lexicon recognizes, so some words differ than the initial top 10 we gatherd earlier. The range of values is the same as the band’s, but we can see that the majority of his most frequent words fall into the negative range.
Harry Styles
Styles has seen the most success out of the five as an independent artist. His first self-titled studio album was released in 2017, consisting of songs he contributed to in writing himself. Styles was also often a contributing writer for One Direction songs and has written for numerous other artists. He released his second album in October 2019. Both of his works have a style different than that of his pop boy-band days. Styles adopted more of his own taste into his music, withmore soft-rock and folk influences along with his pop roots.
First we need to gather his albums and break them down into lyrics.
harry_albums<- tribble(
~artist,~title,
"Harry Styles", "Harry Styles",
"Harry Styles", "Fine Line")
harry_lyrics<-harry_albums %>%
add_genius(artist,title,type="album")Next, I seperated each word and removed the stop words to generate a word cloud of all of the lyrics in his albums and the frequency.
harry_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
count(word,sort=TRUE)->harry_words
wordcloud2(harry_words)harry_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in Harry Styles' Albums") + theme_solarized()+labs(y="Occurrences", x="Word")
The lyrics of Styles’ songs seem to be less generic and more diverse than those by One Direction. The top 10 words used across his two albums also show that he uses the words “da” and “la” quite often, which is often attributed to pop songs. Though listeners of Styles will notice that these sounds moreso match that of a more folk style tune. Nevertheless, the use of filler words reamains a constant between Styles and his former band. Other words, though, are less similar to his boy-band lyrics. We start to see more descriptive and specific words that stray from things like “time”, “night”, and “love” to things like “watermelon”, “golden”, and “angel”. Overall, the content itself does not appear to have a significant difference aside from a few words here and there.
The sentiment of Harry Styles’ lyrics will be looked at in the next few tables and viualizations.
harry_words%>%
inner_join(get_sentiments("afinn"))->harry_sentiment
harry_sentiment %>%
arrange(desc(-value))->harry_neg
harry_sentiment %>%
arrange(desc(value))->harry_pos
harry_neg %>%
head(10)->top10_neg_harry
harry_pos %>%
head(10) -> top10_pos_harry
top10_neg_harry## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 bitch 2 -5
## 2 die 9 -3
## 3 losing 4 -3
## 4 selfish 3 -3
## 5 bad 2 -3
## 6 died 2 -3
## 7 bribe 1 -3
## 8 hate 1 -3
## 9 hating 1 -3
## 10 kills 1 -3top10_pos_harry## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 win 1 4
## 2 wonderful 1 4
## 3 woo 15 3
## 4 adore 9 3
## 5 love 4 3
## 6 happy 2 3
## 7 paradise 2 3
## 8 fine 21 2
## 9 sweet 19 2
## 10 hope 5 2mean(top10_neg_harry$value)## [1] -3.2mean(top10_pos_harry$value)## [1] 2.9There are 79 occurrences of the top 10 most positive words with a mean of 2.9 among Styles’ lyrics. This compares to the 26 top 10 negative words with a mean of -3.2. As we saw with Malik, the difference is most apparent in the negative words. The lowest value we see is a -5, compared to -3 for One Direction. This value is attributed to a curse word, but the rest of his negatively correlated words are still more harsh than those seen for the band, with lyrics such as “die”, “hate”, and “kills”.
harry_sentiment_graph<- harry_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
harry_sentiment_graph <-harry_words %>%
inner_join(get_sentiments("afinn"))
harry_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("Harry Styles Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
The 10 most frequent words in Styles’ songs with an afinn value assigned to them can be seen above. It appears as though Styles falls around the more neutral to negative side. Most of the bars in the chart fall in the red-pink range, indicating either negative or neutral-to low positive sentiment. We also see the range is different from that of One Direction, spanning this time from -3 to 3.
Niall Horan
Next up is Irish band member Niall Horan. Following the band’s speration, Horan followed in the footsteps of his other bandmates and put out music of his own. He released two studio albums, “Flicker” in 2017 and “Heartbreak Weather” in 2020. He describes his music as having “folk-with-pop feel to it” and a few of his singles have made a presence on country radio, particulary his single “Seeing Blind” featuring country star Maren Morris.
Delving into Horan’s music, I will follow the same pattern as before and start out by grabbing his two albums and sperating the lyrics.
niall_albums<- tribble(
~artist,~title,
"Niall Horan", "Flicker",
"Niall Horan", "Heartbreak Weather")
niall_lyrics<-niall_albums %>%
add_genius(artist,title,type="album")Now we will remove the stop words, generate a word cloud, and look at word frequency.
niall_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
count(word,sort=TRUE)->niall_words
wordcloud2(niall_words)niall_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in Niall Horan's Albums") + theme_solarized()+labs(y="Occurrences", x="Word")
Looking at the word cloud, there are a lot of similarities between Horan’s prominent lyrics and that of One Direction. Love and talking about a love interest reamin common themes and he uses a lot of the same generic words such as “time”, “night”, and “heart”. We do see less filler words, with only “yeah” making it in the top 10 most frequent. Other words among the 10 most frequent are again fairly generic and are references towards love and a romantic interest. Based on this alone, it may appear that Horan stayed close to his pop boy-band roots in terms of his lyrics. There is some diversity that we see more of in Horan’s lyrics that lean more towards telling a story compared to that of his former band, which could be a nod to his folk-influence. Even so, the difference is slight to make any definitve conlcusion.
The sentiment of Niall Horan’s lyrics will be looked at in the next few tables and viualizations.
niall_words%>%
inner_join(get_sentiments("afinn"))->niall_sentiment
niall_sentiment %>%
arrange(desc(-value))->niall_neg
niall_sentiment %>%
arrange(desc(value))->niall_pos
niall_neg %>%
head(10)->top10_neg_niall
niall_pos %>%
head(10) -> top10_pos_niall
top10_neg_niall## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 fucked 1 -4
## 2 loose 14 -3
## 3 bad 8 -3
## 4 lost 6 -3
## 5 dead 4 -3
## 6 guilty 3 -3
## 7 hate 3 -3
## 8 losing 3 -3
## 9 die 2 -3
## 10 killing 2 -3top10_pos_niall## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 heavenly 4 4
## 2 fun 2 4
## 3 funny 1 4
## 4 love 62 3
## 5 nice 6 3
## 6 woo 5 3
## 7 beautiful 1 3
## 8 loved 1 3
## 9 honest 10 2
## 10 hope 9 2mean(top10_neg_niall$value)## [1] -3.1mean(top10_pos_niall$value)## [1] 3.1There are 101 occurrences of the 10 most positive words with a mean score of 3.1 and 46 occurences of the 10 most negative words with a mean of -3.1. This indicates that Horan’s lyrics contain words that have more positive associations than negative ones. Also notice that the lowest afinn value is for one occurence of a curse word, again something we won’t find in One Direction’s lyrics. Horan also uses negative words that are more dark than the more generic ones used by the band, such as “dead”, “hate”, and “killing”. I also want to take a look at the sentiment of his most popular words.
niall_sentiment_graph<- niall_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
niall_sentiment_graph <-niall_words %>%
inner_join(get_sentiments("afinn"))
niall_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("Niall Horan Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
Here we see more of a range in values than that of One Direction, with the scale going from -3 to 3. There is ever so slightly more occurences of positivity in his most prominent lyrics compared to the band, but only by a word or so. In general, Horan’s lyrics appear to be fairly balanced between postive and negative sentiment.
Liam Payne
After the band’s split, Liam Payne went on to venture towards producing and remixing songs for other artists. As one of the key song-writers for One Direction, he also put together his own album, entitled “LP1”. His music has been described as pop, R&B, and electronic.
Once again, I will start the analysis for Liam by collecting the lyrics from his album.
liam_lyrics<-genius_album(artist = "Liam Payne", album="LP1")Next, I seperated each word and removed the stop words to generate a word cloud of all of the lyrics in his album and the word frequency.
liam_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
filter(!word %in% c("ed")) %>%
count(word,sort=TRUE)->liam_words
wordcloud2(liam_words)liam_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in Liam Payne's Album") + theme_solarized()+labs(y="Occurrences", x="Word")
The lyrics of Payne’s music looks similar at first glance to that of One Direction with similar use of filler words and talking about love and girls. Looking more closely, though, we see more mature words used throughout his songs, such as “strip”, “bedroom”, and other words that have sexual connotations when put in the context of the whole song. Of the top 10 most frequent words, “yeah” comes in at number one followed by words we often see in pop music and could be seen in songs by One Direction. I did remove the word “ed” from this list as it is not a recognizable word and has no meaning in the context of this analysis.
While the lyrics look to be more mature, I also want to look at sentiment.
liam_words%>%
inner_join(get_sentiments("afinn"))->liam_sentiment
liam_sentiment %>%
arrange(desc(-value))->liam_neg
liam_sentiment %>%
arrange(desc(value))->liam_pos
liam_neg %>%
head(10)->top10_neg_liam
liam_pos %>%
head(10) -> top10_pos_liam
top10_neg_liam## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 shit 3 -4
## 2 ass 2 -4
## 3 damn 1 -4
## 4 die 24 -3
## 5 lost 12 -3
## 6 criminal 6 -3
## 7 bad 4 -3
## 8 hate 4 -3
## 9 faking 1 -3
## 10 kill 1 -3top10_pos_liam## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 win 10 4
## 2 fun 3 4
## 3 funny 1 4
## 4 love 51 3
## 5 beautiful 7 3
## 6 nice 5 3
## 7 woo 4 3
## 8 celebrate 3 3
## 9 loved 3 3
## 10 sexy 3 3mean(top10_neg_liam$value)## [1] -3.3mean(top10_pos_liam$value)## [1] 3.3There are 58 occurences of the ten most negative words with a mean of -3.3 and with -4 as the lowest value. There are 90 occurences of the ten most positive words with a mean of 3.3 and with 4 as the highest value. We overall see more positive than negative lyrics but have a differnt context of negative words in Payne’s lyrics than in One Direction’s, as we have already seen in other band members. Curse words and more graphic lyrics make an appearance, accounting for the words with the lowest afinn score. This also ties into how much more mature Payne’s lyrics have gotten since the departure from the band. We will also look at the sentiment of his most frequent words.
liam_sentiment_graph<- liam_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
liam_sentiment_graph <-liam_words %>%
inner_join(get_sentiments("afinn"))
liam_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("Liam Payne Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
We get a larger scale than that of One Direction’s most frquent words, with the range going from -2 to 4 for Payne’s lyrics. Though, we do see more words falling into the neutral to negative range. There is still not much of a difference to say that Payne got more negative, though, after departing One Direction.
Louis Tomlinson
The last former band member of One Direction is Louis Tomlinson. Tomlinson has seen less success than his bandmates, but nas nonetheless put out music of his own. He is credited for contributing to the writing of most of the songs on One Direction’s albums and took his songwriting career further to create his own album entitles “Walls”, which was released in January 2020. His music is described as Britpop with EDM influences.
To look at Tomlinson’s lyrics, I will start by gathering the lyrics off of “Walls”.
louis_lyrics<-genius_album(artist = "Louis Tomlinson", album="Walls")Next I will seperate the words, remove the stop words, create a word cloud, and look at word frequency.
louis_lyrics %>%
unnest_tokens(word,lyric) %>%
anti_join(stop_words) %>%
count(word,sort=TRUE)->louis_words
wordcloud2(louis_words)louis_words %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=word)) + geom_col()+ coord_flip()+ ggtitle("Top 10 Words in Louis Tomlinson's Album") + theme_solarized()+labs(y="Occurrences", x="Word")
Some of the most common words off of “Walls” are filler words such as “ooh”, “yeah”, and “ah”. We also see similar generic words to that of One Direction’s lyrics such as “time” and “heart”. There does appear to be a lot more diversity in Tomlinson’s lyrics than that of the band as he seems to get more specific. While specificity and diversity in lyrics could point towards maturity, sentiment can give us a better picture.
louis_words%>%
inner_join(get_sentiments("afinn"))->louis_sentiment
louis_sentiment %>%
arrange(desc(-value))->louis_neg
louis_sentiment %>%
arrange(desc(value))->louis_pos
louis_neg %>%
head(10)->top10_neg_louis
louis_pos %>%
head(10) -> top10_pos_louis
top10_neg_louis## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 hell 3 -4
## 2 kill 23 -3
## 3 hate 4 -3
## 4 lost 4 -3
## 5 die 3 -3
## 6 worry 1 -3
## 7 worst 1 -3
## 8 tears 9 -2
## 9 hurt 5 -2
## 10 hurts 4 -2top10_pos_louis## # A tibble: 10 x 3
## word n value
## <chr> <int> <dbl>
## 1 love 13 3
## 2 perfect 3 3
## 3 loved 2 3
## 4 happiness 1 3
## 5 fearless 12 2
## 6 strong 5 2
## 7 mercy 4 2
## 8 chance 3 2
## 9 proud 3 2
## 10 treasures 3 2mean(top10_neg_louis$value)## [1] -2.8mean(top10_pos_louis$value)## [1] 2.4There are 57 occurences of the most negative words with a mean of -2.8 and 49 occurrences of the most positive words, with mean of 2.4. The highest positive afinn value is 3 while the lowest afinn value is -4, attributed to a curse word. Tomlinson uses more strongly negatively associated words more often, with “kill” being the most frequented. In both sentiment lists, though, there are more descriptive words than those found in One Direction songs, especially in the positive sentiment. This could also indicate more writing maturity in his post-band days.
Looking at the sentiment of his most used words could also give us insight into this idea.
louis_sentiment_graph<- louis_words %>%
head(10) %>%
ggplot(aes(word, n)) + geom_col ()
louis_sentiment_graph <-louis_words %>%
inner_join(get_sentiments("afinn"))
louis_sentiment_graph %>%
head(10) %>%
ggplot(aes(reorder(word, n), n, fill=value)) +
geom_col() +
coord_flip() +
ggtitle("Louis Tomlinson Sentiment of Top 10 Words")+
labs(x="Words",y="Occurences")+
theme_solarized()+
scale_fill_gradient(low="red", high="purple")
The scale for Tomlinson’s most frequent sentiment goes from -3 to 3, which is slightly wider than that of One Direction. There appears to be several words that fall into the neutral to negative range with only 4 words being clearly positive, one of which being “yeah”. It looks like Tomlinson uses strong and descriptive language to achieve getting sentiment accross in his music, but does not lean in an obvious direction to either the positive or negative side.
Conclusion
With more artistic and creative freedom working as solo artists, the former members of One Direction all had the ability to make their music their own. Since the band was known for their upbeat, pop songs that were geared towards a young female audience, it would be reasonable to assume that after going solo their music would grow along with them. Based on this analysis, we see that all five band members put out music that contained lyrics that were more diverse in content than that of One Direction as a whole. Less generic and more specific words were used and often more mature langauge, such as curse words and sexual references. Each artist adapted their own style of music which could contribute to the shift in lyrics, though further analysis into the music itself and not just the lyrics would get a better picture of how the each band member drifted towards certain genres. The part of my hypothesis predicting more mature lyrics from the individual members appeared to hold up after looking at the data. The langauge used and the kinds of words that are frequented differ from that of the band. What has not been proven is whether each member got more negative in their lyrics after going solo. While some artists have more albums than others, the mean positive and negative sentiment values are only slightly different across the board from that of One Direction and aren’t enough to make a definitive conlusion. We do see more graphic and descriptive negative words, but that ties into the maturity aspect moreso than overall sentiment. Each band member has fairly balanced lyrics in terms of sentiment and the only major difference we see from One Direction is the content of the lyrics themselves. There is also not one single artist that stands out as differing the most from the band, as both content and sentiment of their lyrics generally are different in their own way. The main takeaway from this analysis is each former member of One Direction found their own voice and wrote songs that matched their individual style with more mature and descriptive content lyrically.