Example of creating n-gram clouds
Raúl García-Castro (rgarcia@fi.upm.es)
Load required libraries.
library(tm)
library(ggplot2)
library(reshape2)
library(wordcloud)
library(RWeka)
# Needed for a bug when calculating n-grams with weka
options(mc.cores=1)Set the working directory to the location of the script and data.
setwd("~/Youtube")Load corpus from local files.
Load the Sentiment polarity dataset version 2.0 from the Movie review data.
Once unzipped, access the positive reviews in the dataset.
path = "./review_polarity/txt_sentoken/"
dir = DirSource(paste(path,"pos/",sep=""), encoding = "UTF-8")
corpus = Corpus(dir)Check how many documents have been loaded.
length(corpus)## [1] 1000Access the document in the first entry.
corpus[[1]]## <<PlainTextDocument (metadata: 7)>>
## films adapted from comic books have had plenty of success , whether they're about superheroes ( batman , superman , spawn ) , or geared toward kids ( casper ) or the arthouse crowd ( ghost world ) , but there's never really been a comic book like from hell before .
## for starters , it was created by alan moore ( and eddie campbell ) , who brought the medium to a whole new level in the mid '80s with a 12-part series called the watchmen .
## to say moore and campbell thoroughly researched the subject of jack the ripper would be like saying michael jackson is starting to look a little odd .
## the book ( or " graphic novel , " if you will ) is over 500 pages long and includes nearly 30 more that consist of nothing but footnotes .
## in other words , don't dismiss this film because of its source .
## if you can get past the whole comic book thing , you might find another stumbling block in from hell's directors , albert and allen hughes .
## getting the hughes brothers to direct this seems almost as ludicrous as casting carrot top in , well , anything , but riddle me this : who better to direct a film that's set in the ghetto and features really violent street crime than the mad geniuses behind menace ii society ?
## the ghetto in question is , of course , whitechapel in 1888 london's east end .
## it's a filthy , sooty place where the whores ( called " unfortunates " ) are starting to get a little nervous about this mysterious psychopath who has been carving through their profession with surgical precision .
## when the first stiff turns up , copper peter godley ( robbie coltrane , the world is not enough ) calls in inspector frederick abberline ( johnny depp , blow ) to crack the case .
## abberline , a widower , has prophetic dreams he unsuccessfully tries to quell with copious amounts of absinthe and opium .
## upon arriving in whitechapel , he befriends an unfortunate named mary kelly ( heather graham , say it isn't so ) and proceeds to investigate the horribly gruesome crimes that even the police surgeon can't stomach .
## i don't think anyone needs to be briefed on jack the ripper , so i won't go into the particulars here , other than to say moore and campbell have a unique and interesting theory about both the identity of the killer and the reasons he chooses to slay .
## in the comic , they don't bother cloaking the identity of the ripper , but screenwriters terry hayes ( vertical limit ) and rafael yglesias ( les mis ? rables ) do a good job of keeping him hidden from viewers until the very end .
## it's funny to watch the locals blindly point the finger of blame at jews and indians because , after all , an englishman could never be capable of committing such ghastly acts .
## and from hell's ending had me whistling the stonecutters song from the simpsons for days ( " who holds back the electric car/who made steve guttenberg a star ? " ) .
## don't worry - it'll all make sense when you see it .
## now onto from hell's appearance : it's certainly dark and bleak enough , and it's surprising to see how much more it looks like a tim burton film than planet of the apes did ( at times , it seems like sleepy hollow 2 ) .
## the print i saw wasn't completely finished ( both color and music had not been finalized , so no comments about marilyn manson ) , but cinematographer peter deming ( don't say a word ) ably captures the dreariness of victorian-era london and helped make the flashy killing scenes remind me of the crazy flashbacks in twin peaks , even though the violence in the film pales in comparison to that in the black-and-white comic .
## oscar winner martin childs' ( shakespeare in love ) production design turns the original prague surroundings into one creepy place .
## even the acting in from hell is solid , with the dreamy depp turning in a typically strong performance and deftly handling a british accent .
## ians holm ( joe gould's secret ) and richardson ( 102 dalmatians ) log in great supporting roles , but the big surprise here is graham .
## i cringed the first time she opened her mouth , imagining her attempt at an irish accent , but it actually wasn't half bad .
## the film , however , is all good .
## 2 : 00 - r for strong violence/gore , sexuality , language and drug contentCreate a bigram wordcloud
Apply transformations to the original corpus. In this case, add to the stop words list the “’s” and “’ve” words.
corpus.ng = tm_map(corpus,removeWords,c(stopwords(),"s","ve"))
corpus.ng = tm_map(corpus.ng,removePunctuation)
corpus.ng = tm_map(corpus.ng,removeNumbers)Use Weka’s n-gram tokenizer to create a TDM that uses as terms the bigrams that appear in the corpus.
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
tdm.bigram = TermDocumentMatrix(corpus.ng,
control = list(tokenize = BigramTokenizer))Extract the frequency of each bigram and analyse the twenty most frequent ones.
freq = sort(rowSums(as.matrix(tdm.bigram)),decreasing = TRUE)
freq.df = data.frame(word=names(freq), freq=freq)
head(freq.df, 20)## word freq
## special effects special effects 171
## star wars star wars 133
## new york new york 131
## even though even though 120
## one best one best 112
## science fiction science fiction 84
## star trek star trek 84
## high school high school 81
## pulp fiction pulp fiction 75
## takes place takes place 72
## ever seen ever seen 68
## one day one day 68
## supporting cast supporting cast 68
## one thing one thing 62
## jackie chan jackie chan 61
## first film first film 60
## years ago years ago 59
## much like much like 58
## seems like seems like 57
## motion picture motion picture 56Choose a nice range of blue colors for the wordcloud.
You can invoke the display.brewer.all function to see the whole palette
pal=brewer.pal(8,"Blues")
pal=pal[-(1:3)]Plot the wordcloud.
wordcloud(freq.df$word,freq.df$freq,max.words=100,random.order = F, colors=pal)
Plot the most frequent bigrams in a bar graph.
ggplot(head(freq.df,15), aes(reorder(word,freq), freq)) +
geom_bar(stat = "identity") + coord_flip() +
xlab("Bigrams") + ylab("Frequency") +
ggtitle("Most frequent bigrams")
Create a trigram wordcloud
To create a trigram wordcloud, the approach is the same but this time we tell the n-gram tokenizer to find trigrams.
TrigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 3, max = 3))
tdm.trigram = TermDocumentMatrix(corpus.ng,
control = list(tokenize = TrigramTokenizer))Extract the frequency of each trigram and analyse the twenty most frequent ones.
freq = sort(rowSums(as.matrix(tdm.trigram)),decreasing = TRUE)
freq.df = data.frame(word=names(freq), freq=freq)
head(freq.df, 20)## word freq
## saving private ryan saving private ryan 39
## good will hunting good will hunting 34
## new york city new york city 29
## robert de niro robert de niro 25
## jay silent bob jay silent bob 22
## tommy lee jones tommy lee jones 22
## thin red line thin red line 21
## know last summer know last summer 20
## one best films one best films 20
## babe pig city babe pig city 18
## samuel l jackson samuel l jackson 17
## world war ii world war ii 16
## blair witch project blair witch project 15
## film takes place film takes place 15
## american history x american history x 14
## william h macy william h macy 13
## dusk till dawn dusk till dawn 12
## little known facts little known facts 12
## natural born killers natural born killers 12
## one best movies one best movies 12Plot the wordcloud.
wordcloud(freq.df$word,freq.df$freq,max.words=100,random.order = F, colors=pal)
Plot the most frequent trigrams in a bar graph.
ggplot(head(freq.df,15), aes(reorder(word,freq), freq)) +
geom_bar(stat="identity") + coord_flip() +
xlab("Trigrams") + ylab("Frequency") +
ggtitle("Most frequent trigrams")