경희대학교 BK21

install.packages("twitteR")

install.packages("KoNLP")

install.packages("wordcloud")

install.packages("qgraph")

library("twitteR")

consumerKey <- "Nj26Do961BvA7fEKA2X6l4MSU"

consumerSecret <- "TiyR37m7fwar5dT1E8cFBHtYIym8vvJl4vGtIssbVbwGFw7ekL"

accessToken <- "869790446251155456-KCpaQ6riybwFJyZTL4gYmuJ4B6CgQ8c"

accessTokenSecret <- "O6bgUTIKkmOtBEHuJW0mciKaRmH9i2vgi9xgh5OemvFzA"

setup_twitter_oauth(consumerKey, consumerSecret, accessToken, 

                    accessTokenSecret)

keyword <- "오버워치"

keyword <- enc2utf8(keyword)

result<-searchTwitter(keyword, lang="ko",n=10000)

library(KoNLP)

library(wordcloud)

library(RColorBrewer)

stuff_to_remove <- c("http[s]?://[[:alnum:].\\/]+", 

                     "@[\\w]*", "<.*>", "'s", "\\(","\\)", 

                     "#",":","ㄱ-ㅎ")

stuff_to_remove <-  paste(stuff_to_remove, sep = "|", collapse="|")

result <- sapply(result, function(x) x$getText())

result

result <- gsub(stuff_to_remove," ", result)

result <- gsub("[ㄱ-ㅎ]"," ",result)

result <- gsub("[^가-힣0-9A-z]"," ",result)

result <- gsub("RT"," ",result)

result <- gsub("https"," ",result)

result <- gsub("_"," ",result)

result

result <- gsub("#"," ",result)

noun <- sapply(result, extractNoun, USE.NAMES = F)

noun

nounVec <- unlist(noun)

nounVec <- Filter(function(x){nchar(x)>=2}, nounVec)

nounVec <- gsub("RT","",nounVec)

nounVec <- gsub("https","",nounVec)

nounFrame <- data.frame(nounVec)

nounFreq <- table(nounFrame)

head(sort(nounFreq, decreasing = T), 100)

pal <- brewer.pal(3, "Set2")

wordcloud(names(nounFreq), freq=nounFreq, scale=c(5.5, 1), rot.per = 0.5, min.freq = 3, random.order = F, random.color=T, colors=pal, max.words = 100)

getwd()

txt <- readLines("KakaoTalkChats3.txt", encoding="UTF-8")

txt

txt <- gsub("[0-9]{4}[년][ ][0-9]{1,2}[월][ ][0-9]{1,2}[일][ ][가-힣]{2}[ ][0-9]{1,2}[:][0-9]{1,2}","",txt)

txt <- gsub("[,][ ]\\S{1,}[ ][:][ ]","",txt)

txt <- gsub("[ㄱ-ㅎ]"," ",txt)

txt <- gsub("[^가-힣0-9A-z]"," ",txt)

txt <- gsub("[A-z]"," ",txt)

noun2 <- sapply(txt, extractNoun, USE.NAMES = F)

noun2

nounVec2 <- unlist(noun2)

nounVec2 <- Filter(function(x){nchar(x)>=2}, nounVec2)

nounFrame2 <- data.frame(nounVec2)

nounFreq2 <- table(nounFrame2)

head(sort(nounFreq2, decreasing = T), 100)

pal <- brewer.pal(3, "Set2")

wordcloud(names(nounFreq2), freq=nounFreq2, scale=c(5.5, 1), rot.per = 0.5, min.freq = 1, random.order = F, random.color=T, colors=pal, max.words = 100)

install.packages("tm")

library(tm)

install.packages("stringr")

library(stringr)

cps = Corpus(VectorSource(noun))

tdm = TermDocumentMatrix(cps)

tdm.matrix = as.matrix(tdm)

word.count = rowSums(tdm.matrix)

word.order = order(word.count, decreasing = T)

freq.word = tdm.matrix[word.order[1:100],]

rownames(tdm.matrix)[word.order[1:100]]

co.matrix = freq.word %*% t(freq.word)

library(qgraph)

qg <- qgraph(co.matrix,labels=rownames(co.matrix),diag=F,layout='spring',edge.color='black',vsize=log(diag(co.matrix))*1.5)

plot(qg)