Load required libraries.

library(tm)
library(ggplot2)

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))

Make an analysis of what words are more frequently associated with others.

Analyse those terms frequently associated with “star”.

asoc.star = as.data.frame(findAssocs(tdm,"star", 0.5))
asoc.star$names <- rownames(asoc.star) 
asoc.star
##                         star                   names
## trek                    0.63                    trek
## enterpris               0.57               enterpris
## picard                  0.57                  picard
## insurrect               0.56               insurrect
## jeanluc                 0.54                 jeanluc
## androidwishingtobehuman 0.50 androidwishingtobehuman
## anij                    0.50                    anij
## bubblebath              0.50              bubblebath
## crewmat                 0.50                 crewmat
## dougherti               0.50               dougherti
## everbut                 0.50                 everbut
## harkonnen               0.50               harkonnen
## homeworld               0.50               homeworld
## iith                    0.50                    iith
## indefin                 0.50                 indefin
## mountaintop             0.50             mountaintop
## plunder                 0.50                 plunder
## reassum                 0.50                 reassum
## ruafro                  0.50                  ruafro
## soran                   0.50                   soran
## unsuspens               0.50               unsuspens
## verdant                 0.50                 verdant
## youthrestor             0.50             youthrestor

Print them in a bar graph.

ggplot(asoc.star, aes(reorder(names,star), star)) +   
  geom_bar(stat="identity") + coord_flip() + 
  xlab("Terms") + ylab("Correlation") +
  ggtitle("\"star\" associations")

Analyse those terms frequently associated with “indiana”.

asoc.indi = as.data.frame(findAssocs(tdm,"indiana", 0.5))
asoc.indi$names <- rownames(asoc.indi) 
asoc.indi
##                       indiana                 names
## ark                      0.74                   ark
## actionmovi               0.70            actionmovi
## brawn                    0.70                 brawn
## diarrhea                 0.70              diarrhea
## engrav                   0.70                engrav
## hieroglyph               0.70            hieroglyph
## hotfudgerockin           0.70        hotfudgerockin
## minecart                 0.70              minecart
## obcpo                    0.70                 obcpo
## professorarcheologist    0.70 professorarcheologist
## registr                  0.70               registr
## sallah                   0.70                sallah
## salsa                    0.70                 salsa
## swordsman                0.70             swordsman
## indi                     0.68                  indi
## selleck                  0.61               selleck
## shorten                  0.57               shorten
## snake                    0.53                 snake

Print them in a bar graph.

ggplot(asoc.indi, aes(reorder(names,indiana), indiana)) +   
geom_bar(stat="identity") + coord_flip() + 
xlab("Terms") + ylab("Correlation") +
ggtitle("\"indiana\" associations")