Text_Mining_Jane_Austenr

library(tm)

## Loading required package: NLP

getReaders()

##  [1] "readDOC"                 "readPDF"                
##  [3] "readPlain"               "readRCV1"               
##  [5] "readRCV1asPlain"         "readReut21578XML"       
##  [7] "readReut21578XMLasPlain" "readTabular"            
##  [9] "readTagged"              "readXML"

getSources()

## [1] "DataframeSource" "DirSource"       "URISource"       "VectorSource"   
## [5] "XMLSource"       "ZipSource"

#install.packages("tidytext")
require(tidytext)

## Loading required package: tidytext

library(janeaustenr)
warp<-austen_books() #creating warp from austen_books()
Corpus1=VCorpus(VectorSource(warp), readerControl = list(language = "eng")) #using VectorSource & VCorpus
# we may inspect(Corpus1)
# We may call Summary(Corpus1)
Corpus1 <- tm_map(Corpus1, removePunctuation)  
Corpus1 <- tm_map(Corpus1, removeNumbers)  
Corpus1 <- tm_map(Corpus1, tolower)
Corpus1 <- tm_map(Corpus1, removeWords, stopwords("english"))
#install.packages("SnowballC")
library(SnowballC)
Corpus1 <- tm_map(Corpus1, stemDocument)  
Corpus1 <- tm_map(Corpus1, stripWhitespace)   
Corpus1 <- tm_map(Corpus1, PlainTextDocument)
dtm <- DocumentTermMatrix(Corpus1)
dtms <- removeSparseTerms(dtm, 0.2)
inspect(dtm[1,1:30])

## <<DocumentTermMatrix (documents: 1, terms: 30)>>
## Non-/sparse entries: 30/0
## Sparsity           : 0%
## Maximal term length: 12
## Weighting          : term frequency (tf)
## 
##               Terms
## Docs           abandoned abashed abate abatement abating abbey abbeyfor
##   character(0)         1       1     1         4       1    62        1
##               Terms
## Docs           abbeyland abbeymil abbeymill abbeyoh abbeys abbots
##   character(0)         1        1         6       1      2      1
##               Terms
## Docs           abbreviation abdication abdys abhor abhorr abhorred
##   character(0)            1          1     1     5      3        3
##               Terms
## Docs           abhorrence abhorrent abide abiding abilities ability able
##   character(0)          8         2     4       1        26       2  290
##               Terms
## Docs           ableand ablehave ableshe ablest
##   character(0)       1        1       1      1

# We may inspect(dtms[1,1:30])
tdm <- TermDocumentMatrix(Corpus1)
tdm

## <<TermDocumentMatrix (terms: 20363, documents: 2)>>
## Non-/sparse entries: 20373/20353
## Sparsity           : 50%
## Maximal term length: 32
## Weighting          : term frequency (tf)

(tdm[1:30,1])

## <<TermDocumentMatrix (terms: 30, documents: 1)>>
## Non-/sparse entries: 30/0
## Sparsity           : 0%
## Maximal term length: 12
## Weighting          : term frequency (tf)

matx1=as.matrix(tdm)
matx1[1:10]

##  [1]  1  1  1  4  1 62  1  1  1  6

sort1=sort(rowSums(matx1),decreasing=T)
sort1[1:10]

##       emma  mansfield       park      pride   prejudic      sense 
##      16987      15533      15501      13127      13033      12804 
##       sens    persuas      abbey northanger 
##      12665       8335       7918       7889

di=data.frame(Word=names(sort1),Frequency=sort1)
di[1:10,]

##                  Word Frequency
## emma             emma     16987
## mansfield   mansfield     15533
## park             park     15501
## pride           pride     13127
## prejudic     prejudic     13033
## sense           sense     12804
## sens             sens     12665
## persuas       persuas      8335
## abbey           abbey      7918
## northanger northanger      7889

#install.packages("wordcloud")
library(wordcloud)

## Loading required package: RColorBrewer

wordcloud(di$Word, di$Frequency, max.words=100,colors=brewer.pal(6, "Reds"))

wordcloud(di$Word, di$Frequency, max.words=100,colors=brewer.pal(6, "Dark2"))

wordcloud(di$Word, di$Frequency, max.words=1000,colors=brewer.pal(6, "Reds"))

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : emma could not be fit on page. It will not be plotted.

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## plotted.

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## brewer.pal(6, : mansfield could not be fit on page. It will not be plotted.

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## brewer.pal(6, : mistaken could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : expressed could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : supposed could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : welcome could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : trouble could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : breakfast could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : answer could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : strange could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : gardiner could not be fit on page. It will not be plotted.

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## brewer.pal(6, : likely could not be fit on page. It will not be plotted.

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## brewer.pal(6, : gone could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : martin could not be fit on page. It will not be plotted.

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## brewer.pal(6, : affect could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : delight could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : tomorrow could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : seem could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : probably could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : half could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : light could not be fit on page. It will not be plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors
## = brewer.pal(6, : acquainted could not be fit on page. It will not be
## plotted.

## Warning in wordcloud(di$Word, di$Frequency, max.words = 1000, colors =
## brewer.pal(6, : brought could not be fit on page. It will not be plotted.

Text_Mining_Jane_Austenr_Novels.R

sanpande

Fri Oct 28 02:01:01 2016