《釜山行》是一部丧尸劫难片，其人物少、干系简朴，很是适合我们进修文本处理惩罚。这个项目将先容共此刻干系中的提取，利用R编写代码实现对《釜山行》文本的人物干系举办阐明。
利用到的R包library(rvest)library(jiebaR)library(stringr)library(readr)library(tidyverse)library(network)library(sna)library(ggnetwork)library(igraph)
抓取《釜山行》脚本url=’http://labfile.oss.aliyuncs.com/courses/677/busan.txt’text=read_html(url)%>%html_text()#对脚本举办分段spltext=str_split(text,’\r\n\r\n \r\n\r\n’)names(spltext) = ‘spltextname’subtext=spltext$spltextname人物干系挖掘思路：若两小我私家物呈此刻同一段则对其权重加1，每段中对应干系呈现几多次加几多次n=length(spltext$spltextname)data=vector(‘list’,n)cutter = worker(‘mix’)for(i in 1:n) data[[i]] = cutter[subtext[i]][which(cutter[subtext[i]] %in% iden)]#which(cutter[subtext1] %in% iden)weidata=data.frame(t(combn(iden,2)))names(weidata)=c(‘name1′,’name2’)weiname=rep(0,136)for(i in 1:136) weiname[i]=paste(weidata$name1[i],weidata$name2[i],sep = ‘–‘)weidata$weiname=weinameweidata$weight = rep(0,136)for(i in 1:72){ if(length(data[[i]]) != 0){ test=as.data.frame(table(paste(expand.grid(data[[i]],data[[i]])$Var1, expand.grid(data[[i]],data[[i]])$Var2,sep = ‘–‘))) test$Var1=as.vector(test$Var1) test$Freq=test$Freq/max(test$Freq) id1=which(test$Var1 %in% weidata$weiname) id2=which( weidata$weiname %in% test$Var1) weidata$weight[id2]=weidata$weight[id2]+test$Freq[id1] }}
功效：
weight=0说明两人没有过寒暄，weight越大干系越密切绘制人物干系网络图taltext=as.data.frame(table(cutter[subtext][which(cutter[subtext] %in% iden)]))taltext$Var1=as.vector(taltext$Var1)ind3=rep(0,17)for(i in 1:17) ind3[i] = taltext$Freq[which(taltext$Var1[i] == iden)]weidata1 = weidata[which(weidata$weight !=0),]g1=graph.data.frame(weidata1[,-3], directed = F)op=par(mar=c(0,0,0,0))plot(g1, edge.width = E(g1)$weight, vertex.size=rank(ind3), layout=layout.fruchterman.reingold, vertex.color= ind3, vertex.label.cex=0.75, vertex.label.color=’blue’ )par(op)
利用ggplot2气势气魄n=fortify(g1)ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) + geom_edges(linetype = 2, color = “grey50”,curvature = 0.1) + geom_nodes(aes(color = vertex.names, size = weight)) + geom_nodelabel_repel(aes(color = vertex.names, label = vertex.names), fontface = “bold”, box.padding = unit(1, “lines”)) + theme(legend.position=’none’, axis.text = element_blank(), axis.title = element_blank(), panel.background = element_rect(fill = “grey25”), panel.grid = element_blank() )
云词图（文本挖掘必备）require(RColorBrewer)library(wordcloud)
#这里的停词是把底层的stop_words.utf-8改为txt名目，改之前先备份wk<-worker(‘mix’, stop_word=”D:/Program Files/R/R-3.3.2/library/jiebaRD/dict/stop_words.txt”)textda=wk[subtext]datext=as.data.frame(table(textda))datext$textda=as.vector(datext$textda)
textdata=arrange(datext,desc(Freq))textdata1=textdata[which(textdata$Freq>5),]pal2 <- brewer.pal(8,”Dark2″) wordcloud(textdata1$textda,textdata1$Freq,colors=pal2,random.order=FALSE,ot.per=.45)
library(wordcloud2)wordcloud2(textdata,color=”random-light”,backgroundColor = ‘black’)
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