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Hands-On Data Science with R

Text Mining

Graham.Williams@togaware.com

10th January2016

Visithttp://HandsOnDataScience.com/formoreChapters.

Text Mining (or Text Analytics) applies analytic tools to learn from collections of text data,

like socialmedia, books,newspapers, emails, etc. The goal canbe consideredtobe similar to

humanslearning by readingsuch material. However, using automated algorithmswe canlearn

frommassiveamountsoftext,verymuchmorethanahumancan. Thematerialcouldconsistof

millionsofnewspaperarticlestoperhapssummarisethemainthemesandtoidentifythosethat

are of most interest to particular people. Or we might be monitoring twitter feedsto identify

emergingtopicsthat we might needtoact upon,asit emerges.

Therequiredpackagesforthischapter include:

library(tm)

# Framework for text mining.

library(qdap)

# Quantitative discourse analysis of transcripts.

library(qdapDictionaries)

library(dplyr)

# Data wrangling, pipe operator %>%().

library(RColorBrewer)

# Generate palette of colours for plots.

library(ggplot2)

# Plot word frequencies.

library(scales)

# Include commas in numbers.

library(Rgraphviz)

# Correlation plots.

Aswe work throughthis chapter,newR commandswill be introduced. Be sure to review the

command’sdocumentationandunderstandwhatthecommanddoes. Youcanaskforhelpusing

the?commandasin:

?read.csv

Wecanobtaindocumentationonaparticularpackageusingthehelp=optionoflibrary():

library(help=rattle)

This chapter is intended to be hands on. To learn eectively, you are encouraged to have R

running (e.g.,RStudio) andto runallthecommandsastheyappear here. Check that youget

thesameoutput,andyouunderstandtheoutput. Trysome variations. Explore.

2013-2015GrahamWilliams. Youcanfreelycopy,distribute,

oradapt thismaterial,aslongastheattributionisretainedandderivative

work isprovidedunder thesamelicense.

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DataScience withR

Hands-On

Text Mining

1 Getting Started: The Corpus

The primary package for text mining, tm (Feinerer and d Hornik, 2015), provides a framework

withinwhichweperform our text mining. Acollectionof other standard Rpackagesaddvalue

tothedataprocessingandvisualizationsfor textmining.

Thebasic conceptisthat of acorpus. Thisisacollectionoftexts,usuallystoredelectronically,

andfrom which we perform our analysis. A corpusmight bea collectionof newsarticlesfrom

Reutersor the publishedworksofShakespeare. Withineachcorpuswe willhaveseparatedocu-

ments,whichmightbearticles,stories,orbookvolumes. Eachdocumentistreatedasaseparate

entityorrecord.

Documentswhichwe wishtoanalyse comeinmany dierent formats. Quite afew formatsare

supportedby tm(FeinererandHornik,2015),thepackagewewillillustratetext miningwithin

thismodule. Thesupportedformatsinclude text,PDF,Microsoft Word,andXML.

Anumberofopensourcetoolsarealsoavailabletoconvertmostdocumentformatstotextles.

Forourcorpususedinitiallyinthismodule,acollectionofPDFdocumentswereconvertedtotext

usingpdftotextfromthexpdfapplicationwhichisavailableforGNU/LinuxandMS/Windows

andothers. OnGNU/Linux wecanconvert afolder ofPDFdocumentstotext with:

system("for f in *.pdf; do pdftotext -enc ASCII7 -nopgbrk $f; done")

The -enc ASCII7 ensuresthe text isconvertedto ASCII since otherwise we may end upwith

binary charactersinour text documents.

We can also convert Word documents to text using anitword, which is another application

available for GNU/Linux.

system("for f in *.doc; do antiword $f; done")

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DataScience withR

Hands-On

Text Mining

1.1 Corpus Sources and Readers

Thereare avariety of sourcessupportedby tm. We canusegetSources()tolistthem.

getSources()

## [1] "DataframeSource" "DirSource"

"URISource"

"VectorSource"

## [5] "XMLSource"

"ZipSource"

Inadditiontodierentkindsofsourcesofdocuments,ourdocumentsfortextanalysiswillcome

inmany dierent formats. Avariety aresupportedby tm:

getReaders()

## [1] "readDOC"

"readPDF"

## [3] "readPlain"

"readRCV1"

## [5] "readRCV1asPlain"

"readReut21578XML"

## [7] "readReut21578XMLasPlain" "readTabular"

## [9] "readTagged"

"readXML"

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DataScience withR

Hands-On

Text Mining

1.2 Text Documents

We load a sample corpus of text documents. Our corpus consists of a collection of research

papers all stored in the folder we identify below. To work along with us in this module, you

can create your own folder called corpus/txt and place into that folder a collection of text

documents. It doesnot needto be as many aswe use here but a reasonable number makesit

moreinteresting.

cname <- file.path(".", "corpus", "txt")

cname

## [1] "./corpus/txt"

Wecanlistsome of the lenames.

length(dir(cname))

## [1] 46

dir(cname)

## [1] "acnn96.txt"

## [2] "adm02.txt"

## [3] "ai02.txt"

## [4] "ai03.txt"

## [5] "ai97.txt"

## [6] "atobmars.txt"

....

Thereare 46documentsinthisparticularcorpus.

Afterloadingthetm(FeinererandHornik,2015)packageintotheRlibrarywearereadytoload

thelesfrom thedirectoryasthe sourceofthelesmakingupthecorpus,usingDirSource().

The source object ispassedonto Corpus()whichloadsthedocuments. We save the resulting

collectionofdocumentsinmemory,storedinavariablecalleddocs.

library(tm)

docs <- Corpus(DirSource(cname))

docs

## <<VCorpus>>

## Metadata: corpus specific: 0, document level (indexed): 0

## Content: documents: 46

class(docs)

## [1] "VCorpus" "Corpus"

class(docs[[1]])

## [1] "PlainTextDocument" "TextDocument"

summary(docs)

Length Class

Mode

## acnn96.txt

PlainTextDocument list

## adm02.txt

PlainTextDocument list

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DataScience withR

Hands-On

Text Mining

## ai02.txt

PlainTextDocument list

## ai03.txt

PlainTextDocument list

## ai97.txt

PlainTextDocument list

....

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DataScience withR

Hands-On

Text Mining

1.3 PDF Documents

IfinsteadoftextdocumentswehaveacorpusofPDFdocumentsthenwecanusethereadPDF()

reader functiontoconvert PDFintotext andhavethat loadedasout Corpus.

docs <- Corpus(DirSource(cname), readerControl=list(reader=readPDF))

This will use, by default, the pdftotext command from xpdf to convert the PDF into text

format. The xpdfapplicationneedstobeinstalledfor readPDF()towork.

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DataScience withR

Hands-On

Text Mining

1.4 Word Documents

A simple open source tool to convert Microsoft Word documents into text is antiword. The

separate antiwordapplicationneedstobeinstalled,but onceit isavailable itisusedby tmto

convert Worddocumentsintotext forloadingintoR.

Toloadacorpusof Worddocumentsweuse the readDOC()reader function:

docs <- Corpus(DirSource(cname), readerControl=list(reader=readDOC))

Oncewe haveloadedour corpustheremainder oftheprocessingofthecorpuswithinRisthen

asfollows.

Theantiwordprogramtakessomeusefulcommandlinearguments. Wecanpassthese through

totheprogramfrom readDOC()byspecifyingthem asthecharacter stringargument:

docs <- Corpus(DirSource(cname), readerControl=list(reader=readDOC("-r -s")))

Here,-rrequeststhatremovedtext beincludedintheoutput,and-srequeststhattexthidden

byWordbe included.

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DataScience withR

Hands-On

Text Mining

2 Exploring the Corpus

Wecan(andshould) inspect the documentsusing inspect(). Thiswillassureusthatdatahas

beenloadedproperly andasweexpect.

inspect(docs[16])

## <<VCorpus>>

## Metadata: corpus specific: 0, document level (indexed): 0

## Content: documents: 1

## [[1]]

## <<PlainTextDocument>>

## Metadata: 7

## Content: chars: 44776

viewDocs <- function(d, n) fd %>% extract2(n) %>% as.character() %>% writeLines()g

viewDocs(docs, 16)

## Hybrid weighted random forests for

## classifying very high-dimensional data

## Baoxun Xu1 , Joshua Zhexue Huang2 , Graham Williams2 and

## Yunming Ye1

## 1

....

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DataScience withR

Hands-On

Text Mining

3 Preparing the Corpus

Wegenerallyneedtoperformsome pre-processingofthetext datatoprepareforthetextanal-

ysis. Exampletransformationsinclude convertingthe texttolowercase,removingnumbersand

punctuation, removing stop words, stemmingand identifyingsynonyms. The basic transforms

areallavailablewithintm.

getTransformations()

## [1] "removeNumbers"

"removePunctuation" "removeWords"

## [4] "stemDocument"

"stripWhitespace"

Thefunctiontm

map()isusedtoapplyoneofthesetransformationsacrossalldocumentswithin

a corpus. Other transformations can be implemented using R functions and wrapped within

content

transformer()tocreate afunctionthat canbepassedthroughtotm

map(). Wewill

see anexampleofthat inthenext section.

Inthe following sectionswe will apply each of the transformations, one-by-one, to remove un-

wantedcharactersfromthetext.

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DataScience withR

Hands-On

Text Mining

3.1 Simple Transforms

Westartwithsomemanualspecialtransformswemaywanttodo. Forexample,wemightwant

toreplace \/",usedsometimestoseparatealternative words, withaspace. Thiswillavoidthe

two wordsbeingruninto onestring of charactersthrough the transformations. We might also

replace\@"and\|"withaspace,forthesame reason.

Tocreateacustomtransformationwemakeuseof content

transformer()tocreateafunction

toachievethetransformation,andthenapply ittothecorpususing tm

map().

toSpace <- content_transformer(function(x, pattern) gsub(pattern, " ", x))

docs <- tm_map(docs, toSpace, "/")

docs <- tm_map(docs, toSpace, "@")

docs <- tm_map(docs, toSpace, "nn|")

Thiscanbe donewithasingle call:

docs <- tm_map(docs, toSpace, "/|@|nn|")

Check the emailaddressinthe following.

inspect(docs[16])

## <<VCorpus>>

## Metadata: corpus specific: 0, document level (indexed): 0

## Content: documents: 1

## [[1]]

## <<PlainTextDocument>>

## Metadata: 7

## Content: chars: 44776

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