Anime Script of One Piece Red and Made in Abyss
Sophia Sanchez
2023-12-13
Getting the Script of Made in Abyss and One Piece Red
Using Sublikescript to get script anime movies such as One Piece Red Movie and Made in Abyss.
Extract Made in Abyss script, check for character,and split into sentece,
Read the Made in Abyss with the html node of the full script. The “.full-script” receive the full text of Made In Abyss script. After the receiving the text, checking the variable if character since the text is string character. Then split the text into sentence to read carefully and the code to read carefully.
Extract One Piece Red script, check for character, and split into sentece
Read the One Piece Red with the html node of the full script. The “.full-script” receive the full text of One Piece Red script. After the receiving the text, checking the variable if character since the text is string character. Then split the text into sentence to read carefully and the code to read carefully.
Cleaning the Made in Abyss with tm Corpus
Using tm is lot easier to clean the text and remove stop words. Replacing the unnecessary words as blanks such as “offthis” or “mitty”
Cleaning the One Piece Red with tm Corpus
Same as Made in Abyss with using the library of tm to clean out junk, stop word, and unnecessary words
#Frequency number of Made in Abyss and One Piece The graph for the frequent words that appear in Made in Abyss and One Piece Red in separate graph.
madein_abyss <- tm_map(AbbysCorpus, stemDocument)## Warning in tm_map.SimpleCorpus(AbbysCorpus, stemDocument): transformation drops
## documentsterm_countabby <- freq_terms(madein_abyss,20)
plot(term_countabby)
one_piece_r <- tm_map(OnePCorpus, stemDocument)## Warning in tm_map.SimpleCorpus(OnePCorpus, stemDocument): transformation drops
## documentsterm_countonep <- freq_terms(one_piece_r,20)
plot(term_countonep)
Creating Document Term Matrix,Sort the Frequency, and plot the most frequent words for Made in Abyss
The Document-Term Matrix that show the relationship which each row as document and the columns as the terms.The top 5 most frequent word is “just” with the count number of 30.
#To Analyze the text data, using the Document-Term Matrix (DTM)
Abby_Made = DocumentTermMatrix(madein_abyss)
inspect(Abby_Made)## <<DocumentTermMatrix (documents: 852, terms: 842)>>
## Non-/sparse entries: 1647/715737
## Sparsity : 100%
## Maximal term length: 20
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs come even get just like now one pleas take time
## 142 0 0 0 0 0 0 0 0 0 0
## 196 0 0 0 0 0 0 0 0 1 0
## 29 0 0 0 1 1 1 0 0 0 0
## 476 0 0 0 0 1 0 0 0 0 0
## 579 0 0 0 0 0 0 0 0 0 0
## 714 0 0 0 0 0 0 0 0 0 0
## 798 0 0 0 0 1 0 0 0 0 0
## 841 0 0 2 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0
## 94 1 0 0 0 0 0 0 0 0 0miabyss_m <- as.matrix(Abby_Made)
#Sort by decreasing value of frequency
miabyss_v <- sort(colSums(miabyss_m),decreasing = TRUE)
miabyss_d <- data.frame(word = names(miabyss_v), freq=miabyss_v)
head(miabyss_d, 5)## word freq
## just just 26
## like like 25
## come come 20
## time time 16
## even even 15#Plot the most frequent words
barplot(miabyss_d[1:5,]$freq, las = 2, names.arg = miabyss_d[1:5,]$word,
col = "lightgreen",main = "Top 5 most frequent words",
ylab = "Word frequencis")
###Creating Document Term Matrix,Sort the Frequency, and plot the most frequent words for One Piece Red The Document-Term Matrix that show the relationship which each row as document and the columns as the terms.The top 5 most frequent word is “let” with the count number of 40.
#Building Document-Term Matrix (DTM)
One_Red = DocumentTermMatrix(one_piece_r)
inspect(One_Red)## <<DocumentTermMatrix (documents: 328, terms: 1388)>>
## Non-/sparse entries: 2901/452363
## Sparsity : 99%
## Maximal term length: 21
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs can cant get just let like new want will world
## 112 0 1 1 1 0 0 2 0 3 2
## 117 0 1 1 3 0 0 0 0 0 0
## 143 0 0 2 0 0 1 0 0 0 0
## 231 0 0 0 0 0 1 0 0 0 0
## 237 1 3 2 1 0 0 0 0 1 0
## 240 0 2 1 3 0 0 2 2 1 0
## 272 3 4 2 0 1 1 0 0 0 0
## 300 1 3 3 1 2 3 1 0 4 4
## 311 0 2 0 0 1 1 1 1 2 2
## 83 0 1 3 2 2 1 0 1 1 0onepiecer_m <- as.matrix(One_Red)
#Sort by decreasing value of frequency
onepiecer_v <- sort(colSums(onepiecer_m),decreasing = TRUE)
onepiecer_d <- data.frame(word = names(onepiecer_v), freq=onepiecer_v)
head(onepiecer_d, 5)## word freq
## let let 41
## can can 38
## want want 36
## cant cant 34
## like like 32#Plot the most frequent words
barplot(onepiecer_d[1:5,]$freq, las = 2, names.arg = onepiecer_d[1:5,]$word,
col = "red",main = "Top 5 most frequent words",
ylab = "Word frequencis")
World Cloud for Made in Abyss and word assocation
#Generate Word Cloud
set.seed(1234)
wordcloud(words = miabyss_d$word, freq = miabyss_d$freq, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
#Word Association
findAssocs(Abby_Made, terms = c("just","like","pleas"), corlimit = 0.25)## $just
## allow are thing mind reli help
## 0.38 0.38 0.31 0.26 0.26 0.26
##
## $like
## much
## 0.29
##
## $pleas
## okayif partway throughyour
## 0.28 0.28 0.28World Cloud for One Piece Redand word assocation
#Generate Word Cloud
set.seed(1234)
wordcloud(words = onepiecer_d$word, freq = onepiecer_d$freq, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
#Word Association
findAssocs(One_Red, terms = c("want","sea","new"), corlimit = 0.25)## $want
## forgetlet piec dream cri happili
## 0.40 0.40 0.32 0.31 0.29
## courseguy danc okayit thisof whenev
## 0.28 0.28 0.28 0.28 0.28
## fansit muchinstead nowdo pirateto pirateyeah
## 0.28 0.28 0.28 0.28 0.28
## pirateyou singerlook togetheral togetherwait yearswhat
## 0.28 0.28 0.28 0.28 0.28
## about bearutta choth gorgi planluffi
## 0.28 0.28 0.28 0.28 0.28
## shanksshut shipi sorrypaypalah wellif adviceso
## 0.28 0.28 0.28 0.28 0.28
## controlled drop happyit innoc itin
## 0.28 0.28 0.28 0.28 0.28
## itstop latewhat plan solv violenc
## 0.28 0.28 0.28 0.28 0.28
## worldwhat worryyou diseas freedomto homewhat
## 0.28 0.28 0.28 0.28 0.28
## liedidnt persecut play live anyth
## 0.28 0.28 0.27 0.26 0.26
## said
## 0.26
##
## $sea
## pirat fansit muchinstead nowdo pirateto pirateyeah
## 0.61 0.56 0.56 0.56 0.56 0.56
## pirateyou singerlook togetheral togetherwait yearswhat decid
## 0.56 0.56 0.56 0.56 0.56 0.52
## luffylet friend care one luffi shes
## 0.52 0.50 0.42 0.41 0.40 0.39
## use defeat two happili piec boxingsor
## 0.39 0.39 0.39 0.39 0.39 0.37
## freedomwhat gave goodlet pirateif piratewhat sakei
## 0.37 0.37 0.37 0.37 0.37 0.37
## shouldnt themtang thingsther redhead ask great
## 0.37 0.37 0.37 0.34 0.32 0.31
## ive minut thing world fun fan
## 0.31 0.31 0.30 0.29 0.29 0.28
## got weak least today also cipher
## 0.26 0.25 0.25 0.25 0.25 0.25
## white abouti
## 0.25 0.25
##
## $new
## era age peopl will long
## 0.70 0.52 0.41 0.41 0.40
## everi white youd problem kill
## 0.38 0.37 0.37 0.37 0.36
## actual agoso airflow arrest assembl
## 0.35 0.35 0.35 0.35 0.35
## author breez cannon cannonmak check
## 0.35 0.35 0.35 0.35 0.35
## code command compani complic concerteveryon
## 0.35 0.35 0.35 0.35 0.35
## condit cook crack dangerousit daughterth
## 0.35 0.35 0.35 0.35 0.35
## detect eligiathirti emerg entir flag
## 0.35 0.35 0.35 0.35 0.35
## flagship follow girlhey gunner hersh
## 0.35 0.35 0.35 0.35 0.35
## herw hole hungrylet ignit interfer
## 0.35 0.35 0.35 0.35 0.35
## ityeah knowcaptain latei lazi level
## 0.35 0.35 0.35 0.35 0.35
## measur meter musica musicthat obstacl
## 0.35 0.35 0.35 0.35 0.35
## overboardi perfect pirateit portantth prepar
## 0.35 0.35 0.35 0.35 0.35
## presenc quicklyth readi reportsa revolut
## 0.35 0.35 0.35 0.35 0.35
## rush sailar seal sendhow shipsif
## 0.35 0.35 0.35 0.35 0.35
## shipstak sight signal songredhead songsal
## 0.35 0.35 0.35 0.35 0.35
## stuffthat support thin tot vicinityther
## 0.35 0.35 0.35 0.35 0.35
## water wave wavesattent weird wheelsno
## 0.35 0.35 0.35 0.35 0.35
## worryingif adviceso controlled drop happyit
## 0.35 0.35 0.35 0.35 0.35
## innoc itin itstop latewhat plan
## 0.35 0.35 0.35 0.35 0.35
## solv violenc worldwhat worryyou creat
## 0.35 0.35 0.35 0.35 0.34
## seem phone whole anyth lot
## 0.34 0.34 0.34 0.34 0.31
## ate use boat send ship
## 0.31 0.31 0.31 0.31 0.31
## sure right hurt bug path
## 0.31 0.30 0.30 0.30 0.29
## food mani sound without youwhat
## 0.29 0.29 0.29 0.29 0.28
## fun make around
## 0.27 0.26 0.26Positive/Negative and Emotion of Made in Abyss
Made in Abyss has lot of negative words and lot of fear since the movie has its scary moments and show words that relates the character’s fear.
# Calculate sentiment scores of Made in Abyss
result <- get_nrc_sentiment(as.character(madein_abyss))
result1<-data.frame(t(result))
Made_in_Abyss <- data.frame(rowSums(result1))
#name rows and columns of the data frame
names(Made_in_Abyss)[1] <- "count"
Made_in_Abyss <- cbind("sentiment" = rownames(Made_in_Abyss), Made_in_Abyss)
rownames(Made_in_Abyss) <- NULL
#plot the first 8 rows,the distinct emotions
qplot(sentiment, data=Made_in_Abyss[1:8,], weight=count, geom="bar",fill=sentiment)+ggtitle("Made in Abyss Sentiments")## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#plot the last 2 rows ,positive and negative
qplot(sentiment, data=Made_in_Abyss[9:10,], weight=count, geom="bar",fill=sentiment)+ggtitle("Made in Abyss Sentiments")
Positive/Negative and Emotion of One Piece Red
This is similar to Made in Abyss of fear and negative. This may result of characters being scare or showing fear through other characters.
# Calculate sentiment scores of Made in Abyss
result2 <- get_nrc_sentiment(as.character(one_piece_r))
result21<-data.frame(t(result2))
One_Piece_Red <- data.frame(rowSums(result21))
#name rows and columns of the data frame
names(One_Piece_Red)[1] <- "count"
One_Piece_Red <- cbind("sentiment" = rownames(One_Piece_Red), One_Piece_Red)
rownames(One_Piece_Red) <- NULL
#plot the first 8 rows,the distinct emotions
qplot(sentiment, data=One_Piece_Red[1:8,], weight=count, geom="bar",fill=sentiment)+ggtitle("One Piece Red Sentiments")
#plot the last 2 rows ,positive and negative
qplot(sentiment, data=One_Piece_Red[9:10,], weight=count, geom="bar",fill=sentiment)+ggtitle("One Piece Red Sentiments")
In conclusion, both anime movies show words that is more negative but still have words that is positive that the character made state in the script. The emotion in characters in Made in Abyss and One Piece Red show a lot fear which indicate the negative words and character said something scary to scare the character.