Danneman_01_mining_twitteR
Danneman
28 September 2015
==================================================================================================================================
Motivational Buckets
set user.hashtag - twitter hashtag, e.g. “#internetmarketing”
set user.numtweets - pull number of tweets, e.g. 1000
set user.min.freq - wordcloud minimum frequency words , e.g. 20
set user.min.corr - find associations minimum correlation, e.g. 0.2
1.1) (size: 26) Authenticate Twitter with your consumer information.
1.2) (size: 7) Search for a hashtag, view Twitter limitation, and convert list to data frame.
1.3) (size: 9) Data munge the Twitter search results into a Corpus, which represents a collection of documents.
1.4) (size: 2) Create and display a wordcloud with a user-specified minimum frequency words.
1.5) (size: 3) Pull up some basic information about a term-document matrix, and view some of the most common terms in it.
1.6) (size: 5) Explore the data in an associational sense by looking at co-location, or those terms that frequently co-occur.
1.7) (size: 7) Alternatively, clustering is another way of finding associations between items. Create and display a dendogram.
1.8) (size: 45) Plot a histogram of sentiment analysis on search results, where score>0 indicates positive.
if( Sys.info()["sysname"] == "Linux" )
source("~/100 FxOption/103 FxOptionVerBack/081 Fx Bit/R-source/PlusReg.R", echo=FALSE)
if( Sys.info()["sysname"] == "Windows" )
source("C:/Users/denbrige/100 FxOption/103 FxOptionVerBack/081 Fx Bit/R-source/PlusReg.R", echo=FALSE)
if( Sys.info()["nodename"] == "GARYLEE-PC" )
RegHomeDir <- function()
{
retDir <- NULL
if( RegIsLinuxBln() )
retDir <- paste0("/home/",Sys.info()["user"],"/")
if( RegIsWindowsBln() )
retDir <- paste0("D:/",Sys.info()["user"],"/")
retDir
}
suppressPackageStartupMessages(require( testthat ))
suppressPackageStartupMessages(require( twitteR ))
suppressPackageStartupMessages(require( base64enc ))
ver <- "0.9.0"
user.hashtag <- "#ebook"
user.numtweets <- 2000
user.min.freq <- 20
user.min.corr <- 0.2
my.key <- "1pfHd50fu7VO8vfdqx4bOkrpo"
my.secret <- "wg5GIzENknyUHjGmC4IMktOe4su5macuhHdzAP0Bw2HFGBMfhc"
access.token <- "3288835615-ubl8PEcQzYHO7q83yj0L0DUGbNmGsvCjOwwRRDr"
access.secret <- "gAlBYpEnVK1aZbNeVnwSElllxHb3ggJpuNpUXjybqOKV9"
setup_twitter_oauth(my.key,my.secret,access.token,access.secret)## [1] "Using direct authentication"1.1) (size: 26) (above) Comment out function RegHomeDir() above, if not using D: drive as the data drive. Create R variables from your own consumer information, then pass these information to a function called setup_twitter_oauth(), which has superceded the function OAuthFactory() from package ROAuth.
bigdata <- searchTwitter(user.hashtag, n=user.numtweets, lang="en")
class(bigdata)## [1] "list"head(bigdata)## [[1]]
## [1] "JanRomes: RT @theLeslieAustin: http://t.co/xU44bIN6OR Check out these book reviews! #BookBoost #AmReading #BookBuzz #mustread #ebook #adventure http:
"
##
## [[2]]
## [1] "smg3979: RT @black_duck_sw: Think You Got Security Covered? Think Again\nGet the #Ebook to see what most testing tools miss http://t.co/K4e8bxinzt ht
"
##
## [[3]]
## [1] "SmarterRegs: .@NOAAFisheries Southwest Fisheries Science Center Fisheries Research #fishing http://t.co/DMJE9bXcbW read #ebook http://t.co/YICvrtI6iG"
##
## [[4]]
## [1] "helenscotttaylo: RT @DebraBurroughs: \"Fast-paced suspense, sweet romance...\" THREE DAYS IN SEATTLE #eBook http://t.co/6HwInjk8io #mgtab http://t.co/zNIQT3e5
"
##
## [[5]]
## [1] "aharemboysaga: RT @PhilipSmithEu: Check out Viewings by @pswinnauthor http://t.co/nO0X9brIN0 #scifi #kindle #ebook http://t.co/afbForv9is"
##
## [[6]]
## [1] "kindlebooks_4u: My Book ''Mindfulness For Beginners'' is free today http://t.co/zs5QTc7P7I #ebook #ebookalert #FREE #freebook #freekindle @KindleFireGuide"bigdata[[4]]## [1] "helenscotttaylo: RT @DebraBurroughs: \"Fast-paced suspense, sweet romance...\" THREE DAYS IN SEATTLE #eBook http://t.co/6HwInjk8io #mgtab http://t.co/zNIQT3e5
"rate.limit <- getCurRateLimitInfo(c("lists"))
rate.limit## resource limit remaining reset
## 1 /lists/list 15 15 2015-09-30 13:07:16
## 2 /lists/memberships 15 15 2015-09-30 13:07:16
## 3 /lists/subscribers/show 15 15 2015-09-30 13:07:16
## 4 /lists/members 180 180 2015-09-30 13:07:16
## 5 /lists/subscriptions 15 15 2015-09-30 13:07:16
## 6 /lists/show 15 15 2015-09-30 13:07:16
## 7 /lists/ownerships 15 15 2015-09-30 13:07:16
## 8 /lists/subscribers 180 180 2015-09-30 13:07:16
## 9 /lists/members/show 15 15 2015-09-30 13:07:16
## 10 /lists/statuses 180 180 2015-09-30 13:07:16bigdata.df <- do.call(rbind, lapply(bigdata, as.data.frame))1.2) (size: 7) (above) Generally speaking, you will be limited to 15 searches every 15 minutes, so make them count. You can access a particular object within a list by using double braces. To limit the number of searches we have to undertake, it can be useful to convert our search results to a data frame and then save them for later analysis.
suppressPackageStartupMessages(require( tm ))
removeNonAscii <- function(x) { iconv(x, "latin1", "ASCII", sub="") }
bigdata.list <- sapply(bigdata, function(x) x$getText())
bigdata.corpus <- Corpus(VectorSource(bigdata.list))
bigdata.corpus <- tm_map(bigdata.corpus, removeNonAscii)
bigdata.corpus <- tm_map(bigdata.corpus, tolower)
bigdata.corpus <- tm_map(bigdata.corpus, removePunctuation)
bigdata.corpus <- tm_map(bigdata.corpus, function(x) removeWords(x,stopwords()))
bigdata.corpus <- tm_map(bigdata.corpus, PlainTextDocument)1.3) (size: 9) (above) A corpus is an abstraction in R to represent a collection of documents. The final four lines of code convert all of the words to lowercase, remove all punctuation, drop stopwords, and TextDocument respectively. Stopwords are very common words that do not carry much meaning, such as ‘a’, ‘are’, ‘that’, and ‘the’. A more complete list of stopwords can be found at http://www.ranks.nl/resources/stopwords.html.
suppressPackageStartupMessages(require( wordcloud ))wordcloud(bigdata.corpus, min.freq=user.min.freq, colors=brewer.pal(8, "Dark2"))
1.4) (size: 2) Create and display a wordcloud with a user-specified minimum frequency words.
bigdata.tdm <- TermDocumentMatrix(bigdata.corpus)
bigdata.tdm## <<TermDocumentMatrix (terms: 4363, documents: 2000)>>
## Non-/sparse entries: 22070/8703930
## Sparsity : 100%
## Maximal term length: 30
## Weighting : term frequency (tf)findFreqTerms(bigdata.tdm, lowfreq=user.min.freq)## [1] "99cents" "action" "adventure"
## [4] "airplane" "alienabduction" "amazon"
## [7] "amp" "amreading" "amwriting"
## [10] "amz0ne" "amzn99" "amznbooks"
## [13] "amzndeal" "amznfavorites" "art"
## [16] "asimov" "asmsg" "asperond"
## [19] "ate" "author" "available"
## [22] "beautiful" "beginners" "best"
## [25] "bestseller" "blog" "book"
## [28] "bookboost" "bookbuzzr" "books"
## [31] "bookworm" "buried" "business"
## [34] "bynr" "california" "can"
## [37] "check" "class" "convert"
## [40] "cost" "craves" "crime"
## [43] "dark" "dear" "dog"
## [46] "download" "early" "ebo"
## [49] "ebook" "ebookalert" "ebooks"
## [52] "emperors" "empire" "epic"
## [55] "erotica" "ethnic" "fantasy"
## [58] "feed" "fiction" "find"
## [61] "follow" "foundation" "free"
## [64] "freebook" "freeebook" "freekindle"
## [67] "garylindberg" "gerty" "get"
## [70] "good" "gratifying" "great"
## [73] "heart" "historical" "horror"
## [76] "htt" "http" "httpt"
## [79] "httptco7hsfvrdugd" "httptco7wngnupryc" "httptcocdixitwysh"
## [82] "httptcoqrmkj4np2z" "httptcor236uecrdb" "httptcowhnrez8y59"
## [85] "httptcowrkivn98oa" "httptcozs5qtc7p7i" "ian1"
## [88] "iartg" "indiewritersupp" "isaac"
## [91] "jamaica" "janinebucks" "japan"
## [94] "johnroseputnam" "kindle" "kindleunlimited"
## [97] "know" "land" "learn"
## [100] "legend" "like" "list"
## [103] "loriedden" "lots" "love"
## [106] "magic" "malic" "man"
## [109] "manning" "marketing" "marsden"
## [112] "mindfulness" "monsters" "mustread"
## [115] "mystery" "never" "new"
## [118] "novel" "now" "one"
## [121] "outnow" "paperback" "paranormal"
## [124] "pdf" "philipsmitheu" "pls"
## [127] "post" "power" "price"
## [130] "promised" "ptmayes" "quotes"
## [133] "read" "reading" "recipes"
## [136] "reg" "regional" "release"
## [139] "revenge" "review" "reviews"
## [142] "romance" "rss" "rubensteinbooks"
## [145] "scifi" "secret" "series"
## [148] "shortstories" "small" "smashwords"
## [151] "sos" "spencer" "spymaster"
## [154] "startup" "story" "suspense"
## [157] "tales" "thriller" "thx"
## [160] "time" "today" "twitter"
## [163] "vanx" "via" "war"
## [166] "warrior" "will" "words"
## [169] "world" "writers" "writing"
## [172] "written" "youll" "young"1.5) (size: 3) Simply calling the name of a term-document matrix pulls up some basic information about it, including the number of terms and documents, the number of non-zero cells, and some other information. Using the findFreqTerms() function, we can view some of the most common terms in our matrix.
assoc.list <- findAssocs(bigdata.tdm, substr(user.hashtag,2,nchar(user.hashtag)), user.min.corr)
assoc.df <- as.data.frame(assoc.list)barplot.bln <- TRUE
barplot.bln <- !is.null(assoc.df) & barplot.bln
barplot.bln <- !RegIsEmptyBln(assoc.df) & barplot.bln
barplot.bln <- nrow(assoc.df)>=1 & barplot.bln
if( barplot.bln==TRUE )
{
par(mar=c(5.1, max(4.1,max(nchar(rownames(assoc.df)))/1.8) ,4.1 ,2.1))
barplot.limit <- min(nrow(assoc.df),15)
barplot.limit <- max(1,barplot.limit)
barplot( rev(assoc.df[,1])[1:barplot.limit], horiz=TRUE, las=2, cex.names=0.8, border=NA, main=paste0("Association - ",user.hashtag),
names.arg=rev(rownames(assoc.df))[1:barplot.limit], col=brewer.pal(8, "Accent"), xlab="Correlation" )
}
1.6) (size: 5) We can also explore the data in an associational sense by looking at co-location, or those terms that frequently co-occur. Each number is the correlation in the term-document matrix between the term it references and the user.hashtag.
bigdata2.tdm <- removeSparseTerms(bigdata.tdm, sparse=0.92)
bigdata2.tdm.insp <- inspect(bigdata2.tdm)## <<TermDocumentMatrix (terms: 6, documents: 2000)>>
## Non-/sparse entries: 3303/8697
## Sparsity : 72%
## Maximal term length: 8
## Weighting : term frequency (tf)
##
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 1 0 0
## ebook 1 1 1 1
## free 0 2 0 0
## freebook 0 1 0 0
## kindle 1 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 1 0 0
## ebook 1 1 1 1
## free 0 0 1 0
## freebook 0 0 1 0
## kindle 0 1 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 1 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 0 1 1 0
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 1 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 1 0
## book 0 0 1 1
## ebook 1 1 1 1
## free 0 0 0 2
## freebook 0 0 1 1
## kindle 1 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 1 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 1
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 1
## kindle 0 1 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 0 1 1
## free 1 0 1 1
## freebook 0 0 0 0
## kindle 0 1 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 1 1 0
## ebook 1 1 1 1
## free 2 0 0 0
## freebook 1 0 0 0
## kindle 0 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 1 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 1
## freebook 0 0 0 0
## kindle 1 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 1 0 0 0
## book 1 1 0 0
## ebook 1 1 1 1
## free 0 2 0 0
## freebook 1 1 0 0
## kindle 1 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 0
## ebook 1 0 0 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 1 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 0 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 1 1
## book 0 0 0 1
## ebook 1 1 1 1
## free 1 0 0 0
## freebook 0 0 1 1
## kindle 0 0 1 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 1 1 0
## ebook 1 2 1 1
## free 0 1 2 0
## freebook 0 0 1 0
## kindle 0 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 0 0 0
## ebook 1 1 1 1
## free 0 1 0 1
## freebook 0 0 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 0 0
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 0 1
## free 0 0 1 0
## freebook 0 0 0 0
## kindle 1 1 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 1 0
## ebook 1 1 1 1
## free 1 1 0 0
## freebook 0 0 0 0
## kindle 0 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 1 0 0
## book 0 0 0 0
## ebook 1 1 2 2
## free 0 0 1 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 1 1 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 2
## ebook 2 1 1 2
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 1 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 2 2 1
## free 2 2 2 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 0 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 1 0
## ebook 2 2 1 0
## free 2 2 2 0
## freebook 0 0 1 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 0 1
## free 0 0 0 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 1 0
## book 0 0 0 0
## ebook 1 1 1 0
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 0 0 0
## ebook 1 1 1 0
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 2
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 1 1 1
## free 2 0 0 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 1 1 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 1 0 2
## free 2 0 0 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 2 2 2
## free 2 2 2 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 2
## free 0 0 0 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 1
## ebook 2 2 0 1
## free 2 2 0 2
## freebook 0 0 0 1
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 2
## free 1 1 0 2
## freebook 0 0 0 0
## kindle 0 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 2 1 2
## free 2 2 0 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 1 2 2
## free 2 0 2 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 2 2 1 2
## free 2 2 0 2
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 1 0 0
## ebook 2 1 2 1
## free 2 0 2 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 1 0 0
## ebook 1 1 0 1
## free 0 2 0 0
## freebook 0 1 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 2 1 1
## free 0 2 1 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 1 0
## ebook 1 1 1 1
## free 1 1 2 0
## freebook 0 0 1 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 1 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 0 0 0
## ebook 1 0 1 1
## free 2 0 0 0
## freebook 1 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 1 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 1 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 0 1 1 2
## free 0 1 0 0
## freebook 0 0 0 0
## kindle 0 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 1 0
## book 0 0 0 0
## ebook 1 0 1 0
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## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 1 0
## ebook 1 1 1 1
## free 1 1 2 1
## freebook 0 0 1 1
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 0
## free 0 1 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 0
## ebook 1 1 1 0
## free 0 0 1 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 1
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 1 0 0 0
## book 0 1 1 0
## ebook 2 1 1 1
## free 0 2 0 0
## freebook 0 1 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 0
## ebook 1 1 0 2
## free 1 0 0 0
## freebook 1 0 0 0
## kindle 0 0 0 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 0 0 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 1 0 0
## ebook 1 1 1 1
## free 0 2 0 0
## freebook 0 1 0 0
## kindle 1 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 2 1
## free 0 0 0 1
## freebook 0 0 0 0
## kindle 0 0 1 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 0 1 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 0 1 1
## free 1 1 0 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 1
## book 0 0 0 0
## ebook 1 0 1 1
## free 0 1 1 0
## freebook 0 0 0 0
## kindle 1 0 1 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 0 0 0
## ebook 1 1 1 1
## free 0 0 1 0
## freebook 0 0 0 0
## kindle 0 0 1 1
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 1 0 0
## book 1 0 1 0
## ebook 1 2 1 1
## free 0 0 2 1
## freebook 0 0 1 0
## kindle 0 2 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 1 1 0 0
## ebook 1 1 1 1
## free 2 0 1 0
## freebook 1 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 1 1
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 1 1 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 1 1 0 0
## freebook 0 0 0 0
## kindle 0 0 0 2
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 0 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 0
## Docs
## Terms character(0) character(0) character(0) character(0)
## amazon 1 0 0 0
## book 0 0 0 0
## ebook 1 1 1 1
## free 0 0 0 0
## freebook 0 0 0 0
## kindle 0 0 0 1bigdata2.df <- as.data.frame(bigdata2.tdm.insp,stringsAsFactors=FALSE)
bigdata2.df.scale <- scale(bigdata2.df)
bigdata2.dist <- dist(bigdata2.df.scale, method="euclidean")
bigdata2.fit <- hclust(bigdata2.dist, method="ward.D")cluster.bln <- TRUE
cluster.bln <- !is.null(bigdata2.fit) & cluster.bln
cluster.bln <- !RegIsEmptyBln(bigdata2.fit) & cluster.bln
cluster.bln <- length(bigdata2.fit)>=1 & cluster.bln
if( cluster.bln )
plot(bigdata2.fit, main=paste0("Cluster - ",user.hashtag))
1.7) (size: 7) Clustering, in general, is a way of finding associations between items. This necessitates a measure of how far each observation is from every other one. Nearby observations are binned together in groups, whereas the ones further apart are put into separate groups. We use a variant method called hierarchical agglomerative clustering. Interested readers can find out more about clustering methods at http://www.statmethods.net/advstats/cluster.html. First, remove the sparsest terms to reduce the length of the term-document matrix dramatically without losing relations inherent in the matrix. Second, convert the term-document matrix to a data frame. Third, we scale the data frame because clustering is sensitive to the scale of the data used. Specifically, the scale() function subtracts every element in a vector from the vector’s mean and divides each element by the vector’s standard deviation. Fourth, we compute a distance matrix, where each row is a document and each column is the same set of documents. Each cell represents the distance between each pair of documents. The last two lines cluster, draw blue boxes and plot the data. The preceding dendrogram shows our clusters in a tree diagram. The bottom row shows individual observations with groupings increasing in size as we traverse up the traverse up the tree.
suppressPackageStartupMessages(require( plyr ))
suppressPackageStartupMessages(require( stringr ))
hu.liu.pos <- scan(paste0(RegRNonSourceDir(),"positive-words.txt"), what='character', comment.char=';')
hu.liu.neg <- scan(paste0(RegRNonSourceDir(),"negative-words.txt"), what='character', comment.char=';')
pos.word <- c(hu.liu.pos, "lol")
neg.word <- c(hu.liu.neg, "wtf")
score.sentence <- function(sentence, pos.words, neg.words)
{
# clean up sentences with R's regex-driven global substitute, gsub():
sentence = gsub('[[:punct:]]', '', sentence)
sentence = gsub('[[:cntrl:]]', '', sentence)
sentence = gsub('\\d+', '', sentence)
# and convert to lower case:
sentence = tolower(sentence)
# split into words. str_split is in the stringr package
word.list = str_split(sentence, '\\s+')
# sometimes a list() is one level of hierarchy too much
words = unlist(word.list)
# compare our words to the dictionaries of positive & negative terms
pos.matches = match(words, pos.words)
neg.matches = match(words, neg.words)
# match() returns the position of the matched term or NA
# we just want a TRUE/FALSE:
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
# and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
score = sum(pos.matches) - sum(neg.matches)
return(score)
}
score.sentiment <- function(sentences, pos.words, neg.words)
{
# we got a vector of sentences. plyr will handle a list
# or a vector as an "l" for us
# we want a simple array of scores back, so we use
# "l" + "a" + "ply" = "laply":
scores = lapply(sentences, score.sentence, pos.words, neg.words)
scores.df = data.frame(score=as.matrix(scores), text=sentences)
return(scores.df)
}
bigdata.df <- dataframe<-data.frame(text=unlist(sapply(bigdata.corpus, `[`, "content")), stringsAsFactors=F)suppressPackageStartupMessages(require( ggplot2 ))
hist.bln <- TRUE
hist.bln <- !is.null(bigdata.df) & hist.bln
hist.bln <- !RegIsEmptyBln(bigdata.df) & hist.bln
hist.bln <- nrow(bigdata.df)>=1 & hist.bln
if( hist.bln )
{
bigdata.score <- unlist(score.sentiment(bigdata.df[,1], pos.word, neg.word)[,1])
par(mar=c(5.1, 4.1 ,4.1 ,2.1))
qplot( x,data=data.frame( x=bigdata.score, pos=bigdata.score>0 ), geom="histogram", fill=pos, binwidth=0.5,
main=paste0("Hu & Liu's Sentiment - ",user.hashtag), xlab="Score", ylab="Tweet Frequency" )
}
if( hist.bln )
{
score.max <- max(bigdata.score)
score.min <- min(bigdata.score)
score.limit <- max(abs(score.max),abs(score.min))+1
score.step <- round( (score.limit*2)/(length(bigdata.score)-1)+0.5 )
par(mar=c(5.1, 4.1 ,4.1 ,2.1))
hist( bigdata.score, main=paste0("Hu & Liu's Sentiment - ",user.hashtag), xlab="Score", ylab="Tweet Frequency",
col="blue", las=1, breaks=seq(-score.limit, score.limit, by=score.step) )
}