Load required libraries.

library(tm)
library(ggplot2)
library(reshape2)

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] 1000

Access 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 content

Define custom stop words for our corpus.

myStopwords = c(stopwords(),"film","films","movie","movies")

Create a TDM with the transformations and the custom stop words.

tdm = TermDocumentMatrix(corpus,
                         control=list(stopwords = myStopwords,
                                      removePunctuation = T, 
                                      removeNumbers = T,
                                      stemming = T))

Since we have a highly sparse TDM, remove sparse terms.

tdm.small = removeSparseTerms(tdm,0.5)
dim(tdm.small)
## [1]   28 1000
tdm.small
## <<TermDocumentMatrix (terms: 28, documents: 1000)>>
## Non-/sparse entries: 16910/11090
## Sparsity           : 40%
## Maximal term length: 7
## Weighting          : term frequency (tf)

See how the new TDM is less sparse.

inspect(tdm.small[1:5,1:5])
## <<TermDocumentMatrix (terms: 5, documents: 5)>>
## Non-/sparse entries: 12/13
## Sparsity           : 52%
## Maximal term length: 7
## Weighting          : term frequency (tf)
## 
##          Docs
## Terms     cv000_29590.txt cv001_18431.txt cv002_15918.txt cv003_11664.txt
##   also                  0               0               0               0
##   can                   1               0               0               2
##   charact               0               1               0               0
##   come                  0               2               1               3
##   end                   3               0               1               0
##          Docs
## Terms     cv004_11636.txt
##   also                  1
##   can                   2
##   charact               2
##   come                  0
##   end                   1

Create a matrix to count all the appearances of terms in the documents.

matrix.tdm = melt(as.matrix(tdm.small), value.name = "count")
head(matrix.tdm)
##     Terms            Docs count
## 1    also cv000_29590.txt     0
## 2     can cv000_29590.txt     1
## 3 charact cv000_29590.txt     0
## 4    come cv000_29590.txt     0
## 5     end cv000_29590.txt     3
## 6    even cv000_29590.txt     3

Plot the word-document frequency graph.

The grey color means that the term does not appear in the document. Besides, a stronger red color indicates a higher frequency.

ggplot(matrix.tdm, aes(x = Docs, y = Terms, fill = log10(count))) +
  geom_tile(colour = "white") +
  scale_fill_gradient(high="#FF0000" , low="#FFFFFF")+
  ylab("Terms") +
  theme(panel.background = element_blank()) +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())