Ziling Zhen
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On this page

  • Introduction
  • Genres of Movies
  • Locations
  • Length & Content of Titles
    • Length of Title
    • Sequels
    • Sentiment: Joy and Sadness
    • Sentiment: Fear and Trust
    • Sentiment: Positive/Negative trajectory of Top 4 Genres by Year
  • LDA 2-topic model
  • Conclusion

Text Analysis of Movies & TV Shows of Streaming Services

Mini-project for SDS 264 - Data Science 2 where we use stingr functions and other text analysis methods
Author

Ziling Zhen

Published

November 20, 2024

This project uses R to create explore the frequency of words in titles and genres of movies and tv shows of different streaming services by creating visuals such as bar plots, wordclouds and tables.

To view the qmd, click here.

Introduction

Do you ever wonder specifically what types of movies or tv shows are on the streaming services you pay for? We will be exploring a comprehensive collection of all titles on Amazon Prime, Apple TV, HBO MAX, Hulu, and Netflix using data collected from IMDB and published on Kaggle. Binding together the rows of these datasets, we end up with 120,930 rows and 9 columns. Each row being title, and columns representing the type, being movie or tv show, the year it was released, genre of the title, imdb rating, the streaming service, etc.

Code
# function for plotting since we have to do it twice
count_plot <- function(variable, fill){
  #labs for title
  title_label <- rlang::englue("Number of Titles per {{variable}}") 
  
  #labs for x axis
  xaxis <- rlang::englue("{{variable}}")
  
  #the plot
  big_data |>
  group_by({{variable}}) |>
  count({{variable}}) |>
  ggplot(aes(x = fct_reorder({{variable}}, -n), y = n, fill = {{fill}})) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  #adds numbers to bar
  geom_text(aes(label = n)) + 
  labs(
    title = title_label,
    x = xaxis,
    y = "Count"
  ) + 
  theme_bw()
}

colors <- c("Netflix" = "#E50914", "AppleTv" = "#808080", "Hulu" = "#1ce783", "HBO" = "#991EEB",
            "AmazonPrime" = "#1399FF")

title_count <- count_plot(service, service) +
  scale_fill_manual(values = colors)

type_count <- count_plot(type, n) +
  scale_fill_gradient2()

# aligning grids in html using cowplot
plot_grid(title_count, type_count, ncol = 1, align = "v") 

We can see that for most titles to least, it ranks Amazon Prime, Netflix, Apple TV, Hulu and HBO. From our 120,930 titles, we have more movies than tv shows.

Genres of Movies

For genres of movies we will be using a str_split() to split up the lists of genres associated with our titles. Some of these genres are also “Action & Adventure” or “War & Politics”, meaning we also have to split the ampersand as well as the comma. After splitting our genres we find that we have 26 distinct genres. We’ll now look what the most popular genres are.

Code
# new tibble for count of genres by type
genres_count <- big_data |> 
  
  # splitting by commas, and ampersand
  mutate(genres = str_split(genres, ", | & ")) |> 
  
  # "unlisting"
  unnest(genres) |> 
  
  # getting rid of no genres
  drop_na(genres) |> 
  group_by(genres, type, service) |>
  
  # count genres for movies and tv shows for service
  count(genres) |> 
  ungroup()
Code
genres_count |>
  group_by(service) |>
  
  # filter for movie by service
  filter(type == "movie") |> 
  slice_max(n, n = 10) |>
  ggplot(aes(y = reorder_within(genres, n, service), x = n, fill = service)) +
    geom_bar(stat = "identity", show.legend = FALSE) +
    facet_wrap(~ service, scales = "free") +
    scale_y_reordered() +
    scale_fill_manual(values = colors) +
  labs(
    title = "Top Genres 10 of Movies by Streaming Services",
    y = "Genre",
    x = "Count"
  ) + 
  theme_bw()

Code
genres_count |>
  group_by(service) |>
  
  # filter for tv by service
  filter(type == "tv") |> 
  slice_max(n, n = 10) |>
  ggplot(aes(y = reorder_within(genres, n, service), x = n, fill = service)) +
    geom_bar(stat = "identity", show.legend = FALSE) +
    facet_wrap(~ service, scales = "free") +
    scale_y_reordered() +
    scale_fill_manual(values = colors) +
  labs(
    title = "Top Genres 10 of TV Shows by Streaming Services",
    y= "Genre",
    x = "Count"
  ) + 
  theme_bw()

The genres are all pretty similar for movies between our streaming services with Drama, Comedy, and Action taking the top 3 for most titles with that genre, and Thriller, Romance, and Adventure for 4th, 5th and 6th, depending on the streaming service. For TV shows we see more of a variety for top 10 genres, we see an increase in Animation, Reality-TV, Crime, Documentary, and Family, however the genres are still very similar by streaming service.

Locations

Using another str_split() we can split up the lists of locations that the title is available to, titles like The Queen’s Gambit is available to countries such as AD, AE, AG, AL, AO, AR, AT, AU, AZ, BA, BB, BE, BG and much more. After splitting our genres we find that we have 138 distinct countries that our streaming services are available in. We’ll now look what countries offer the most titles.

Code
distinctcountry <- big_data |>
  mutate(country = str_split(availableCountries, ", ")) |>
  unnest(country) |>
  distinct(country)

big_data |>
  mutate(country = str_split(availableCountries, ", ")) |>
  unnest(country) |>
  count(country) |>
  slice_max(n, n = 10) |>
  ggplot(aes(x = fct_reorder(country, -n), y = n, fill = n)) +
    geom_bar(stat = "identity", show.legend = FALSE) +
  scale_fill_gradient_tableau() +
  labs(
    title = "Top 10 Locations",
    y = "Count",
    x = "Country"
  ) + 
  theme_bw()

The country with the most titles available is the US, then followed by Great Britain and Guernsey.

Code
big_data |>
  mutate(country = str_split(availableCountries, ", ")) |>
  unnest(country) |>
  group_by(service) |>
  count(country) |>
  slice_max(n, n = 10) |>
  ggplot(aes(x = reorder_within(country, -n, service), y = n, fill = service)) +
    geom_bar(stat = "identity", show.legend = FALSE) +
    facet_wrap(~ service, scales = "free") +
    scale_x_reordered() +
    scale_fill_manual(values = colors) +
  labs(
    title = "Top 10 Locations for Titles Available by Service",
    y = "Count",
    x = "Country"
  ) + 
  theme_bw()

This is very interesting because I did not know that Hulu is only available in the US and Japan before this project. We can see that Japan has more titles available compared to the US. The US is the has the most titles available for Apple TV and Amazon Prime. Slovakia for Netflix, and Nicaragua for HBO.

Length & Content of Titles

Length of Title

For the length of the titles, I am interested if titles the are longer or shorter over time, we will do this by looking at the word count of the titles and the actual character length of the title. By using str_length() we can calculate the how many characters are in the title and str_count() to count the number of words in our title.

Code
# new tbl for numerical data of title
nums_big_data <- big_data |>
  
  # calculates length
  mutate(title_length = str_length(title), 
         title_lower = str_to_lower(title),
         
         # looks for words
         num_count = str_count(title_lower, "\\w+"), 
         
         # matches all numbers in title
         has_number = str_extract_all(title_lower, "\\d+") 
         )
Code
# function for scatter plot
lengthword_plot <- function(xaxis, yaxis, linemethod){
  nums_big_data |>
  group_by(releaseYear) |>
  drop_na() |>
  # mean of both to use
  summarize(avg_words = mean(num_count),
            avg_title = mean(title_length)) |>
  ggplot(aes(x = {{xaxis}}, y = {{yaxis}})) +
  geom_point() +
  geom_smooth(se = FALSE, show.legend = FALSE, formula = 'y ~ x', method = linemethod)
}

# plot for release year and average length of title
length_plot <- lengthword_plot(releaseYear, avg_title, loess) + 
  labs(
    title = "Average Length of Titles by Year",
    x = "Year",
    y = "Average Length of Title"
  ) +
  theme_bw()

# plot for release year and average number of words of title
word_plot <- lengthword_plot(releaseYear, avg_words, loess) + 
  labs(
    title = "Average Number of Words of Titles by Year",
    x = "Year",
    y = "Average Number of Words"
  ) +
  theme_bw()

# comparing avg words, and avg length
word_length <- lengthword_plot(avg_words, avg_title, lm) + 
  labs(
    title = "Average Length by Average Number of Words of Titles",
    x = "Average Number of Words",
    y = "Average Length"
  ) +
  theme_bw()

top_row <- plot_grid(length_plot, word_plot)

plot_grid(top_row, word_length, ncol = 1)

The average length of the title stays consistent over time from average length of 17ish characters in 1900 to 2024. We can see that our line of best fit is nearly identical since the length is depends on the words. The average title is around 3.4 words in 2024. This means that the average word is around 5 characters since the average length of our title is 17 in 2024 and the average number of words is 3.4. However, these plots don’t tell us too much important information about our titles other than the fact that they are consistently around 3 to 4 words throughout the years.

Something more exciting would be exploring the content our titles, these are the top ten most common words in our titles.

rank word n
1 love 2165
2 christmas 1477
3 2 1401
4 life 990
5 story 954
6 la 832
7 de 822
8 night 779
9 world 754
10 movie 728

We can see that “love” is our most common word, appearing 2165 times and then “christmas” appearing 1477 times as second most common. However, something interesting is that “2” is the third most common, this could mean that there are lots of sequels! If you watch a movie or show you enjoy it’s very likely your streaming service also contains the sequel. Let’s see what other numbers are common in our titles. These are the top 10 numbers that show up in our titles.

rank has_number n
1 2 1513
2 3 694
3 1 348
4 4 265
5 10 218
6 7 206
7 5 199
8 13 173
9 9 135
10 12 123

Sequels

Let’s see how many of these titles with a 2 in them are sequels and which sequels are available on our streaming services. Looking at the list of titles with 2’s there were some common occurrences with how second movies are title. We see that there are titles that have a 2 and a colon, such as Super Size Me 2: … or Goosebumps 2: …, then there are the ‘Vol. 2’, ‘VOL. 2’ and ‘Volume 2’. We also see some titles with ‘Part 2’ or ‘Part 2:’ however our OR expression will take care of the ones with a colon. Lastly, we have the titles ending with ‘2’, like Despicable Me 2 or Shrek 2. This makes sure that we don’t pick up titles like 2 Fast 2 Furious, 2:22 or 2 Women. Using our regular expression we are able locate from our 1513 titles with 2’s in them 1240 titles that are sequels. Below is a table of all the sequels available from the 5 streaming services, see if you can find a sequel you recognize.

Code
sequels <- has_2 |>
  mutate(sequel = str_detect(title, " 2: |[vV][oO][lL].2|[vV]olume.2|[mM]ovie.2|[pP]art.2| 2$"))  |>
  filter(sequel == TRUE) 
title service
Predator 2 Hulu
Gremlins 2: The New Batch Hulu
Lethal Weapon 2 Hulu
Die Hard 2 Hulu
Speed 2: Cruise Control Hulu
Under Siege 2: Dark Territory Hulu
Sister Act 2: Back in the Habit Hulu
Mad Max 2: The Road Warrior Hulu
Transporter 2 Hulu
Spy Kids 2: Island of Lost Dreams Hulu
Ju-On: The Grudge 2 Hulu
Ring 2 Hulu
The Hills Have Eyes 2 Hulu
Police Story 2 Hulu
Dr. Dolittle 2 Hulu
The Princess Diaries 2: Royal Engagement Hulu
Child's Play 2 Hulu
Book of Shadows: Blair Witch 2 Hulu
Big Momma's House 2 Hulu
Ghost in the Shell 2: Innocence Hulu
Harry Potter and the Deathly Hallows: Part 2 Hulu
One Missed Call 2 Hulu
Patlabor 2: The Movie Hulu
Bleach the Movie 2: The DiamondDust Rebellion Hulu
Fearless Hyena 2 Hulu
Project A 2 Hulu
InuYasha the Movie 2: The Castle Beyond the Looking Glass Hulu
20th Century Boys 2: The Last Hope Hulu
Nana 2 Hulu
Ip Man 2 Hulu
Sex and the City 2 Hulu
Always: Sunset on Third Street 2 Hulu
Urusei Yatsura 2: Beautiful Dreamer Hulu
Camp Rock 2: The Final Jam Hulu
Elite Squad 2: The Enemy Within Hulu
The Conjuring 2 Hulu
Silent Night, Deadly Night Part 2 Hulu
Nightmare Detective 2 Hulu
Pretty Cure All Stars DX 2: Kibou no Hikari - Rainbow Jewel o Mamore! Hulu
Gamera 2: Attack of the Legion Hulu
Gonin 2 Hulu
The Expendables 2 Hulu
Mondo Cane 2 Hulu
Despicable Me 2 Hulu
Kaiji 2: The Ultimate Gambler Hulu
Grown Ups 2 Hulu
Project A-Ko 2: Plot of the Daitokuji Financial Group Hulu
Hotel Transylvania 2 Hulu
Nana to Kaoru: Chapter 2 Hulu
The Locker 2 Hulu
Sergeant Keroro Super Movie 2: The Deep Sea Princess de arimasu! Hulu
Pretty Cure All Stars New Stage 2: Kokoro no Tomodachi Hulu
Ted 2 Hulu
Phone Call to the Bar 2 Hulu
Dead Snow 2: Red vs. Dead Hulu
8000 Miles 2: Girl Rappers Hulu
The Lego Movie 2: The Second Part Hulu
A Class to Remember 2 Hulu
Now You See Me 2 Hulu
Ada: Part 2 Hulu
John Wick: Chapter 2 Hulu
Kill Zone 2 Hulu
Solomon's Perjury 2 Hulu
Ultraman Zearth 2 Hulu
The Equalizer 2 Hulu
Paddington 2 Hulu
Haikyuu!! The Movie 2: The Winner and the Loser Hulu
Digimon Adventure tri. Part 2: Determination Hulu
Free and Easy 2 Hulu
The Monkey King 2 Hulu
March of the Penguins 2: The Next Step Hulu
64: Part 2 Hulu
The Secret Life of Pets 2 Hulu
Midnight Diner 2 Hulu
Laughing Under the Clouds: Gaiden Part 1 & 2 Hulu
March Comes in Like a Lion 2 Hulu
Yamikin doggusu 2 Hulu
Brotherhood of Blades 2 Hulu
The Tag-Along 2 Hulu
Yakuza Ladies 2 Hulu
Yakuza Ladies Revisited 2 Hulu
Deep Blue Sea 2 Hulu
Tomodachi Game 2 Hulu
High & Low: The Movie 2 - End of Sky Hulu
Shock Wave 2 Hulu
SUPER Horrifying Story 2 Hulu
Trinity Seven The Movie 2: Heavens Library & Crimson Lord Hulu
Sonic the Hedgehog 2 Hulu
Stolen Identity 2 Hulu
Gekijouban Seitokai Yakuindomo 2 Hulu
Santa Baby 2: Christmas Maybe Hulu
Monster Family 2 Hulu
Kyō Kara Ore Wa!! 2 Hulu
Princess Principal Crown Handler: Chapter 2 Hulu
Vacation Friends 2 Hulu
Kingdom 2: Far and Away Hulu
GACHI-BAN: SUPREMACY 2 Hulu
Bokura no nanoka-kan sensô 2 Hulu
Black Angels 2 Hulu
Tales of Terror from Tokyo: Volume 2 Hulu
Ultraman Cosmos 2: The Blue Planet - Young Musashi Chapter Hulu
The Prince of Tennis Best Games!! VOL.2 Hulu
Food, Inc. 2 Hulu
Mobile Detectives: The Movie 2 Hulu
Barikowa 2: Norowareta Onna-tachi Hulu
True Scary Story - Accident Property Entertainer 2 Hulu
Once You Understand It 2 Hulu
I Really Don't Want to Hear it! Horror Stories from the Mountains: Part 2 Hulu
Tales of Terror: G Men Adventure Part 2 Hulu
Nihon Tōitsu Gaiden: Yamazaki Ichimon 2 Hulu
Teen Mom 2 Hulu
Keizoku 2: SPEC - Keishichou kouanbu kouan daigoka mishou jiken tokubetsu taisakugakari jikenbo Hulu
Phantasy Star Online 2: Episode Oracle Hulu
Terminator 2: Judgment Day Netflix
Kill Bill: Vol. 2 Netflix
Spider-Man 2 Netflix
Jaws 2 Netflix
Torrente 2: Mission in Marbella Netflix
Taxi 2 Netflix
American Pie 2 Netflix
Under Siege 2: Dark Territory Netflix
Scream 2 Netflix
Scary Movie 2 Netflix
Rush Hour 2 Netflix
Step Up 2: The Streets Netflix
Mad Max 2: The Road Warrior Netflix
Transporter 2 Netflix
Final Destination 2 Netflix
Ju-On: The Grudge 2 Netflix
Ring 2 Netflix
Legally Blonde 2: Red, White & Blonde Netflix
Police Story 2 Netflix
Armour of God 2: Operation Condor Netflix
Stuart Little 2 Netflix
Scooby-Doo 2: Monsters Unleashed Netflix
Child's Play 2 Netflix
My Girl 2 Netflix
Big Momma's House 2 Netflix
Ghost in the Shell 2: Innocence Netflix
Harry Potter and the Deathly Hallows: Part 2 Netflix
The Scorpion King 2: Rise of a Warrior Netflix
Open Season 2 Netflix
Ong Bak 2 Netflix
Naruto the Movie 2: Legend of the Stone of Gelel Netflix
Bleach the Movie 2: The DiamondDust Rebellion Netflix
The Bodyguard 2 Netflix
Sex Is Zero 2 Netflix
A Chinese Odyssey: Part 2 - Cinderella Netflix
20th Century Boys 2: The Last Hope Netflix
Cocaine Cowboys 2 Netflix
Recep Ivedik 2 Netflix
French Fried Vacation 2 Netflix
Nana 2 Netflix
Ip Man 2 Netflix
Sex and the City 2 Netflix
Phobia 2 Netflix
Attack the Gas Station! 2 Netflix
Demons 2 Netflix
Don 2 Netflix
Always: Sunset on Third Street 2 Netflix
Paranormal Activity 2 Netflix
The Comics 2 Netflix
Race 2 Netflix
Elite Squad 2: The Enemy Within Netflix
Bang Rajan 2 Netflix
Marrying the Mafia 2: Enemy-in-Law Netflix
Pee Nak 2 Netflix
Danur 2: Maddah Netflix
Sing 2 Netflix
The Platform 2 Netflix
Indian 2 Netflix
Too Old for Fairy Tales 2 Netflix
R.I.P.D. 2: Rise of the Damned Netflix
The Conjuring 2 Netflix
The Twilight Saga: Breaking Dawn - Part 2 Netflix
Sacred Demijohn 2 Netflix
El ratón Pérez 2 Netflix
Och, Karol 2 Netflix
Never Back Down 2: The Beatdown Netflix
Journey 2: The Mysterious Island Netflix
The Expendables 2 Netflix
The Smurfs 2 Netflix
Ex 2: Still Friends? Netflix
Taken 2 Netflix
How to Train Your Dragon 2 Netflix
Desperadas 2 Netflix
Insidious: Chapter 2 Netflix
My Tutor Friend 2 Netflix
Golden Chicken 2 Netflix
The Amazing Spider-Man 2 Netflix
Kaiji 2: The Ultimate Gambler Netflix
Grown Ups 2 Netflix
Anchorman 2: The Legend Continues Netflix
Cloudy with a Chance of Meatballs 2 Netflix
The Protector 2 Netflix
Gangs of Wasseypur - Part 2 Netflix
Dibu 2: La venganza de Nasty Netflix
The Chanting 2 Netflix
The Hunger Games: Mockingjay - Part 2 Netflix
2 + 2 Netflix
2 + 2 Netflix
Nativity 2: Danger in the Manger! Netflix
RED 2 Netflix
Iron Ladies 2 Netflix
Puella Magi Madoka Magica the Movie Part 2: Eternal Netflix
The Romancing Star 2 Netflix
Kamenák 2 Netflix
Hotel Transylvania 2 Netflix
Head Over Heels 2 Netflix
Paper Boat 2 Netflix
Romantik Komedi 2: Bekarliga Veda Netflix
The Raid 2 Netflix
A Haunted House 2 Netflix
Osuofia in London 2 Netflix
KL Gangster 2 Netflix
Tales from the Dark Part 2 Netflix
Horrible Bosses 2 Netflix
Till Luck Do Us Part 2 Netflix
Muita Calma Nessa Hora 2 Netflix
Friend 2 Netflix
Satya 2 Netflix
Black mic-mac 2 Netflix
Hello Yasothorn 2 Netflix
Pitch Perfect 2 Netflix
The Reunion 2: The Funeral Netflix
Paul Blart: Mall Cop 2 Netflix
Zum Zum Zum 2: It'll Happen to You Too Netflix
Babovresky 2 Netflix
The Lego Movie 2: The Second Part Netflix
Iris 2: The Movie Netflix
Parasyte: Part 2 Netflix
Sinister 2 Netflix
Now You See Me 2 Netflix
My Big Fat Greek Wedding 2 Netflix
The U: Part 2 Netflix
Qualquer Gato Vira-Lata 2 Netflix
Little Book of Love 2 Netflix
Ride Along 2 Netflix
John Wick: Chapter 2 Netflix
Neighbors 2: Sorority Rising Netflix
Confidential Assignment 2: International Netflix
Suck Me Shakespeer 2 Netflix
Zoolander 2 Netflix
Village Rockstars 2 Netflix
Nothing to Lose 2 Netflix
Babysitting 2 Netflix
Windstorm 2 Netflix
¡Asu Mare! 2 Netflix
The Nut Job 2: Nutty by Nature Netflix
Any Body Can Dance 2 Netflix
99 Cahaya di Langit Eropa Part 2 Netflix
The Holy Man 2 Netflix
Wkreceni 2 Netflix
The Equalizer 2 Netflix
Paddington 2 Netflix
Haikyuu!! The Movie 2: The Winner and the Loser Netflix
Meu Passado Me Condena 2: O Filme Netflix
Spanish Affair 2 Netflix
Mano po 2: My home Netflix
Baahubali 2: The Conclusion Netflix
Pyaar Ka Punchnama 2 Netflix
Ex-Files 2: The Backup Strikes Back Netflix
Dügün Dernek 2: Sünnet Netflix
The 3 L'il Pigs 2 Netflix
Laskar Pelangi Sekuel 2: Edensor Netflix
Comic 8: Casino Kings Part 2 Netflix
The Monkey King 2 Netflix
What's Up with Cinta 2 Netflix
Creep 2 Netflix
Long Long Time Ago 2 Netflix
Hard Target 2 Netflix
Take Off 2 Netflix
Surf's Up 2: WaveMania Netflix
Cold War 2 Netflix
The Heaven None Missed 2 Netflix
De père en flic 2 Netflix
Bon Cop Bad Cop 2 Netflix
Børning 2 Netflix
Reunion 2: The Bachelor Party Netflix
Soof 2 Netflix
Force 2 Netflix
Midnight Diner 2 Netflix
Bulan Terbelah di Langit Amerika 2 Netflix
Servant of the People 2 Netflix
Chastnoe pionerskoe 2 Netflix
Escape Plan 2: Hades Netflix
Goosebumps 2: Haunted Halloween Netflix
The Angry Birds Movie 2 Netflix
Judwaa 2 Netflix
Brotherhood of Blades 2 Netflix
Aladdin 2 Netflix
Catatan Si Boy 2 Netflix
Warkop DKI Reborn: Jangkrik Boss Part 2 Netflix
My Brother 2 Netflix
Verses of Love 2 Netflix
Don't Breathe 2 Netflix
The Wedding Party 2: Destination Dubai Netflix
The Bachelor 2 Netflix
Deep Blue Sea 2 Netflix
Deliha 2 Netflix
Monster Hunt 2 Netflix
Three Little Pigs 2 Netflix
Student of the Year 2 Netflix
Serial (Bad) Weddings 2 Netflix
Detective Chinatown 2 Netflix
High & Low: The Movie 2 - End of Sky Netflix
O Renascimento do Parto 2 Netflix
Kochira Katsushika-ku Kameari kôen mae hashutsujo: The Movie 2 UFO - Shûrai! Torunêdo daisakusen!! Netflix
Gintama 2: Rules are Made to be Broken Netflix
Peter Rabbit 2: The Runaway Netflix
The Accidental Detective 2: In Action Netflix
How High 2 Netflix
Action Figures 2 Netflix
O Candidato Honesto 2 Netflix
Coconut the Little Dragon 2: Into the Jungle Netflix
Munafik 2 Netflix
The Shamer's Daughter 2: The Serpent Gift Netflix
Travel Mates 2 Netflix
Planeta Singli 2 Netflix
Cine Holliúdy 2: A Chibata Sideral Netflix
Polis Evo 2 Netflix
Swingers 2 Netflix
The 3rd Eye 2 Netflix
Khun Pan 2 Netflix
The Divine Move 2: The Wrathful Netflix
Bhai - Vyakti Ki Valli 2 Netflix
The Kissing Booth 2 Netflix
The House Next Door: Meet the Blacks 2 Netflix
My Stupid Boss 2 Netflix
Hajwala 2 Netflix
Uriyadi 2 Netflix
The Blue Elephant 2 Netflix
Kuntilanak 2 Netflix
Si Doel the Movie 2 Netflix
Manmadhudu 2 Netflix
Steel Rain 2 Netflix
Sonic the Hedgehog 2 Netflix
Minions & More 2 Netflix
Love for Sale 2 Netflix
Stolen Identity 2 Netflix
Meg 2: The Trench Netflix
Hamka & Siti Raham Vol. 2 Netflix
Misfit 2 Netflix
Bikeman 2 Netflix
Murder Mystery 2 Netflix
Crazy Wedding 2 Netflix
#FriendButMarried 2 Netflix
Ghost Writer 2 Netflix
Bhool Bhulaiyaa 2 Netflix
Like a Cat on a Highway 2 Netflix
Merry Men 2 Netflix
Stand by Me Doraemon 2 Netflix
Benyamin the Troublemaker 2 Netflix
The Diam Diam Era 2 Netflix
The Beginning of Life 2: Outside Netflix
Tall Girl 2 Netflix
Cemara's Family 2 Netflix
Dragon Inn Part 2 : The Night of Salvation Netflix
King of Prison 2: The Prison War Netflix
Po cem muzi touzí 2 Netflix
Enola Holmes 2 Netflix
Keramat 2: Caruban Larang Netflix
Happy New Year 2 Netflix
Luccas Neto em: O Mapa do Tesouro 2 Netflix
Nobody Sleeps in the Woods Tonight 2 Netflix
The Claus Family 2 Netflix
Love Machine 2 Netflix
OMG 2 Netflix
Piep*zyć Mickiewicza 2 Netflix
Brother in Love 2 Netflix
Luccas Neto em: O Hotel Mágico 2 Netflix
Making the Witcher: Season 2 Netflix
Chief Daddy 2: Going for Broke Netflix
Lost Bullet 2: Back for More Netflix
Believer 2 Netflix
Kingdom 2: Far and Away Netflix
4 Kings 2 Netflix
Chandramukhi 2 Netflix
Les déguns 2 Netflix
Finding Hubby 2 Netflix
Dream Girl 2 Netflix
Love You My Arrogance 2 Netflix
Street Flow 2 Netflix
Monster High 2 Netflix
Pinkfong Sing-Along Movie 2: Wonderstar Concert Netflix
Porady na zdrady 2 Netflix
Tesciowie 2 Netflix
Check Store Next Door 2 Netflix
Tokyo Revengers 2: Bloody Halloween - Destiny Netflix
Tokyo Revengers 2: Bloody Halloween - Decisive Battle Netflix
Katakulli 2: Gözükaralar Netflix
A Stork's Journey 2 Netflix
The Seven Deadly Sins: Grudge of Edinburgh Part 2 Netflix
LSD 2: Love, Sex Aur Dhokha 2 Netflix
LSD 2: Love, Sex Aur Dhokha 2 Netflix
Moo 2 Netflix
Lust Stories 2 Netflix
Mortal World 2 Netflix
Love Tactics 2 Netflix
Anak Perjanjian Syaitan 2 Netflix
Miami Bici 2 Netflix
Life as It Should Be 2 Netflix
The Taming of the Shrewd 2 Netflix
In Good Hands 2 Netflix
Para Betina Pengikut Iblis: Part 2 Netflix
Mathu Vadalara 2 Netflix
The Quiet Don: Chapter 2 Netflix
Losing Lerato 2 Netflix
Go Go Squid 2: DT, Appledog's Time Netflix
Mohamed Ali Road 2 Netflix
Spider-Man 2 HBO
Shrek 2 HBO
Gremlins 2: The New Batch HBO
Lethal Weapon 2 HBO
Taxi 2 HBO
American Pie 2 HBO
Rush Hour 2 HBO
RoboCop 2 HBO
Mad Max 2: The Road Warrior HBO
Free Willy 2: The Adventure Home HBO
Final Destination 2 HBO
A Nightmare on Elm Street 2: Freddy's Revenge HBO
Miss Congeniality 2: Armed & Fabulous HBO
Police Academy 2: Their First Assignment HBO
Stuart Little 2 HBO
Scooby-Doo 2: Monsters Unleashed HBO
My Girl 2 HBO
Harry Potter and the Deathly Hallows: Part 2 HBO
Open Season 2 HBO
Sex and the City 2 HBO
Minuscule 2: Mandibles from Far Away HBO
The Legend of the Christmas Witch 2: The Origins HBO
R.I.P.D. 2: Rise of the Damned HBO
The Conjuring 2 HBO
The Twilight Saga: Breaking Dawn - Part 2 HBO
Kick-Ass 2 HBO
Fimfárum 2 HBO
Journey 2: The Mysterious Island HBO
The Expendables 2 HBO
The Smurfs 2 HBO
Catcher: Cat City 2 HBO
Taken 2 HBO
How to Train Your Dragon 2 HBO
Back in the Hood: Gang War 2 HBO
Insidious: Chapter 2 HBO
The Amazing Spider-Man 2 HBO
Grown Ups 2 HBO
Cloudy with a Chance of Meatballs 2 HBO
The Hunger Games: Mockingjay - Part 2 HBO
Batman: The Dark Knight Returns, Part 2 HBO
Hotel Transylvania 2 HBO
The Raid 2 HBO
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Now, we have a new tibble of just the titles that are sequels from our dataset. We already know that since Amazon Prime has the most titles, it’s very likely that it will also have the most sequels, So, let’s also look at which streaming service has the highest proportion of sequels.

Code
sequels |>
  group_by(service) |>
  count() |>
  ggplot(aes(x = fct_reorder(service, -n), y = n, fill = service)) +
    geom_bar(stat = "identity", show.legend = FALSE) +
    geom_text(aes(label = n)) +
    scale_fill_manual(values = colors) +
    theme() + 
    labs(
     title = "Sequels by Streaming Service",
     x = "Service",
     y = "Count"
   ) +
  theme_bw()

Code
big_data |>
  mutate(sequel = str_detect(title, " 2: |[vV][oO][lL].2|[vV]olume.2|[mM]ovie.2|[pP]art.2| 2$")) |>
  group_by(service) |>
  
  # calculate proportion
  summarize(proportion_sequels = mean(sequel, na.rm = TRUE)) |>
  ggplot(aes(x = fct_reorder(service, -proportion_sequels), 
             y = proportion_sequels, 
             fill = service)) +
  geom_bar(stat = "identity", 
           show.legend = FALSE) +
  geom_text(aes(label = round(proportion_sequels, 4))) +
  scale_fill_manual(values = colors) + 
  theme() + 
  labs(
     title = "Proportion of Sequels by Streaming Service",
     x = "Service",
     y = "Proportion"
     ) +
  theme_bw()

We can see that even though Amazon Prime has the most titles that are sequels, it doesn’t mean it has the highest proportion of sequels. Netflix has the highest proportion with 0.015% of the titles on Netflix are sequels.

Sentiment: Joy and Sadness

Now we’ll look at the list of the top 75 words associated with “joy” and with “sadness” (according to the “nrc” lexicon) across all the titles.

Code
nrc_sentiments <- get_sentiments(lexicon = "nrc")

get_sentiment <- function(emotion1, emotion2){
  nrc_sentiments |>
    
  # filter for two emotions we want
  filter(sentiment == {{emotion1}} | sentiment == {{emotion2}}) |>
  
  # join back in with all words from title 
  inner_join(big_data_tidy, relationship = "many-to-many", by = join_by(word)) |>
  count(sentiment, word, sort = TRUE) |>
  group_by(sentiment) 
}

#use function
joy_sad <- get_sentiment("joy", "sadness")

joy_sad_cloud <- joy_sad |>
  slice_max(n, n = 75) |>
  
  # create color variable 
  mutate(color = ifelse(sentiment == "joy", "lightpink", "lightblue")) |>
  ungroup()
Code
set.seed(2222)
wordcloud(
    words = joy_sad_cloud$word,
    freq = joy_sad_cloud$n,
    random.order = FALSE,
    ordered.colors = TRUE,
    color = joy_sad_cloud$color,
    scale = c(3.5, 1),
    rot.per = .50)

Are there more words associated with joy or with sadness in our titles?

Code
joy_sad |>
  count(sentiment) |>
  ggplot(aes(x = sentiment, y = n)) +
  geom_bar(stat = "identity", fill = c("lightpink", "lightblue")) +
  geom_text(aes(label = n)) +
  labs(
    title = "Count of Joy and Sadness",
    x = "Sentiment",
    y = "Count"
  ) +
  theme_bw()

There are more about 200 more words associated with sadness compared to joy in our titles.

Sentiment: Fear and Trust

What about words associated with fear and trust?

Code
#use function
fear_trust <- get_sentiment("fear", "trust")

fear_trust_cloud <- fear_trust |>
  slice_max(n, n = 75) |>
  # create color variable 
  mutate(color = ifelse(sentiment == "fear", "#CE2029", "#228B22")) |>
  ungroup()

set.seed(2222)
wordcloud(
    words = fear_trust_cloud$word,
    freq = fear_trust_cloud$n,
    random.order = FALSE,
    ordered.colors = TRUE,
    color = fear_trust_cloud$color,
    scale = c(2.5, .5),
    rot.per = .50)

Are there more words associated with joy or with sadness in our titles?

Code
fear_trust |>
  count(sentiment) |>
  ggplot(aes(x = sentiment, y = n)) +
  geom_bar(stat = "identity", fill = c("#CE2029", "#228B22")) +
  geom_text(aes(label = n)) +
  labs(
    title = "Count of Fear and Trust",
    x = "Sentiment",
    y = "Count"
  ) +
  theme_bw()

There are more about 120 more words associated with fear compared to trust in our titles.

Sentiment: Positive/Negative trajectory of Top 4 Genres by Year

The bing lexicon dataset has 6,786 words that are identidied as negative or postive, for the genres Action, Comedy, Drama, and Thriller we’re going to count the number of negative or positive words and calculate the what we call the sentiment value which is the number of positive words minus the negative words. Our bar will extend up from 0 if there are more positive than negative words in a year and it will extend down from 0 if there are more negative than positive words.

Code
bigger_data_tidy <- big_data_tidy |>
  mutate(genres = str_split(genres, ", | & ")) |> 
  unnest(genres) |> 
  drop_na(genres)

# load in bing sentiments
bing_sentiments <- get_sentiments(lexicon = "bing")

bing_sentiments |>
  inner_join(bigger_data_tidy, relationship = "many-to-many", by = join_by(word)) |>
  
  # count by genre, year, sentiment
  count(genres, releaseYear, sentiment, sort = TRUE) |>
  pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) |>
  
  #create sentiment value
  mutate(sentiment = positive - negative) |>
  filter(genres %in% c("Drama", "Comedy", "Action", "Thriller")) |>
  drop_na() |>
  ggplot(aes(x = releaseYear, y = sentiment, fill = genres)) +
  geom_col(show.legend = FALSE) +
    facet_wrap(~genres, ncol = 2, scales = "free_x") +
  labs(
    title = "Sentiment Trajectory of Top 4 Genres by Year",
    x = "Release Year",
    y = "Sentiment Value"
  ) +
  theme_bw()

We can see that for Action and Thriller the sentiment values are very negative, and for Drama there are 2 years in which the titles have more postive words than negative. For Comedy, it is almost split between negative and positive, which is nice to see since most would consider comedic movie or tv show be light-hearted.

LDA 2-topic model

The last thing we will do is create LDA model. LDA stands for Latent Dirichlet allocation, it one of the most common algorithms for topic modeling. We need two things to make it work.

  1. Every document is a mixture of topics, this is where each row represents one document (such as a book or article), in our case it would be a genre. Each document can contain words from several topics. For example, in a two-topic model we could observe “Document 1 is 75% topic A and 25% topic B, while Document 2 is 34% topic A and 66% topic B.”

  2. Every topic is a mixture of words, imagine a two-topic model with sports, Each topic would have its own set of words that define it. Here’s an example of what these could look like:

Topic 1 Words (Competitive & Physical Sports): “win”, “competition”, “athlete”, “team”, “championship” “playoff”

Topic 2 Words (Lifestyle & Recreational Sports): “fitness” “health” “leisure” “adventure” “outdoor” “relaxation”

However, it is important to note that words can be shared between topics; a word like “game”, strength” or “exercise” might appear in both topics equally.

For our dataset, we are interested in our genres and how the distribution of words within the titles of each genre reflects the themes and characteristics of different types of movies and TV shows. After filtering for stop words we will create a dtm for our words and genres, fit our lda model and then interpret our findings.

Code
# creating dtm matrix
bigger_data_dtm <- bigger_data_tidy |>
  filter(!word %in% stop_words$word,
         !is.na(word)) |>
  group_by(genres) |>
  count(word) |>
  cast_dtm(genres, word, n)

# fitting the model
bigger_data_lda <- LDA(bigger_data_dtm, k = 2, control = list(seed = 2222))

bigger_data_topics <- tidy(bigger_data_lda, matrix = "beta")

A table of our highest beta value, which is the probability of a word being generated by a specific topic.

topic term beta
1 love 0.0131613
1 christmas 0.0109698
1 2 0.0070230
2 story 0.0056093
1 movie 0.0054861
2 2 0.0048709
2 night 0.0045407
2 life 0.0041466
2 murder 0.0037594
2 death 0.0037197
2 dead 0.0036993
1 world 0.0035741
2 black 0.0035626
2 love 0.0034933
2 house 0.0033621
2 la 0.0032057
1 girl 0.0031935
2 war 0.0031325
1 time 0.0031234
2 killer 0.0031170
1 life 0.0030860
2 blood 0.0030330
2 dark 0.0029887
2 day 0.0028704
1 3 0.0028054
1 de 0.0027416
2 de 0.0027393
1 dragon 0.0027049
2 world 0.0027040
2 american 0.0026708
2 girl 0.0026363
1 la 0.0024738
1 day 0.0023251
1 king 0.0022515
2 red 0.0021842
1 night 0.0021557
2 die 0.0021549
2 kill 0.0021489
2 evil 0.0021345
1 story 0.0020911
1 lost 0.0020588
1 super 0.0020567
2 mystery 0.0020322
1 legend 0.0020276
1 family 0.0019956
2 days 0.0019723
1 city 0.0019418
2 el 0.0019196
2 game 0.0019081
2 time 0.0018915
Code
# graph of words
bigger_beta_wide |>
  arrange(desc(abs(log_ratio))) |>
  slice_max(abs(log_ratio), n = 20) |>
  mutate(term = reorder(term, log_ratio)) |>
  ggplot(aes(log_ratio, term, fill = log_ratio > 0)) +
    geom_col(show.legend = FALSE) +
    labs(x = "Log Ratio of Beta values",
         y = "Words",
         title = "Top 20 Words with Greatest Log Ratio between Topic 1 & 2") +
  theme_bw()

These are the words with the greatest difference between the two topics, using the log ratio, looking at these words we can see sort of where these topics are leading us.

document topic gamma
Adventure 1 0.9999974
Romance 1 0.9999973
Animation 1 0.9999970
Family 1 0.9999953
Fantasy 1 0.9999939
Musical 1 0.9999743
Game-Show 1 0.9999133
Comedy 1 0.9996166
Adult 1 0.9987842
Kids 1 0.9823630
Documentary 2 0.9999980
Crime 2 0.9999974
Thriller 2 0.9999969
Horror 2 0.9999969
Mystery 2 0.9999951
Biography 2 0.9999926
History 2 0.9999921
War 2 0.9999807
Film-Noir 2 0.9997906
News 2 0.9997875

Topic 1 (red): Documents heavily associated with Topic 1 include genres like Adventure, Romance, Animation, Family, Fantasy, Musical, Game-Show, Comedy, Adult, and Kids. This topic seems to represent genres that are typically focused on entertainment, family-friendly content, and lighter themes. There is a range of genres that cater to various age groups and include both animated and live-action content. Topics in this category could focus on fun, adventure, humor, and fictional worlds.

Topic 2 (blue): Documents heavily associated with Topic 2 include genres like Documentary, Crime, Thriller, Horror, Mystery, Biography, History, War, Film-Noir, and News. This topic seems to capture genres that are more serious, dramatic, and often grounded in real-world issues, with a focus on storytelling, historical events, real-life experiences, and suspense. These genres often explore darker, more complex, or informative themes.

Conclusion

In conclusion, this analysis provides an exploration of the genres and characteristics of movie and TV show titles across major streaming services such as Amazon Prime, Apple TV, HBO MAX, Hulu, and Netflix. By examining over 120,000 titles, we identified patterns in genre distribution, locations available, title length trends over time, and sentiment variations across genres.

The results reveal that Drama, Comedy, and Action dominate the movie genres across all platforms, with TV shows offering a broader variety including Animation, Reality-TV, and Documentary.

Our topic modeling analysis further distinguishes two overarching themes: family-friendly entertainment genres like Comedy and Adventure, and serious, real-world genres like Crime and Documentary. This highlights the diversity in content and the ability of streaming platforms to cater to both lighthearted and thought-provoking preferences.

We gain a better understanding of the types of content available on streaming services by extracting data from the titles and plotting the results.

© Ziling Zhen, 2024

 

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