R-wordcloud: 词云图

library(tm)library(wordcloud)
Paper1<-paste(scan("Paper1.txt", what = character(0),sep = ""), collapse = " ") #读入TXT 文档1Paper2<-paste(scan("Paper2.txt", what = character(0),sep = ""), collapse = " ") #读入TXT 文档2tmpText<- data.frame(c(Paper1, Paper2),row.names=c("Text1","Text2"))df_title <- data.frame(doc_id=row.names(tmpText),                       text=tmpText$c.Paper1..Paper2.)ds <- DataframeSource(df_title)#创建一个数据框格式的数据源，首列是文档id(doc_id),第二列是文档内容corp <- VCorpus(ds)#加载文档集中的文本并生成语料库文件corp<- tm_map(corp,removePunctuation) #清除语料库内的标点符号corp <- tm_map(corp,PlainTextDocument) #转换为纯文本corp <- tm_map(corp,removeNumbers) #清除数字符号corp <- tm_map(corp, function(x){removeWords(x,stopwords())}) #过滤停止词库term.matrix <- TermDocumentMatrix(corp)#利用TermDocumentMatrix()函数将处理后的语料库进行断字处理，生成词频权重矩阵
term.matrix <- as.matrix(term.matrix) #频率colnames(term.matrix) <- c("Paper1","paper2")df<-data.frame(term.matrix)write.csv(df,'term_matrix.csv') #导出两篇文章的频率分析结果

df<-read.csv('term_matrix.csv',header=TRUE,row.names=1)#图3-9-3(a): 单篇文章Paper1数据的展示wordcloud(row.names(df) , df$Paper1 , min.freq=10,col=brewer.pal(8, "Dark2"), rot.per=0.3 )#图3-9-3(b): 单篇文章Paper2数据的展示wordcloud(row.names(df) , df$Paper2 , min.freq=10,col=brewer.pal(8, "Dark2"), rot.per=0.3 )
#两篇文章数据的对比#图3-9-4(a)：两篇文章的独有部分comparison.cloud(df,max.words=300,random.order=FALSE,colors=c("#00B2FF", "red"))#图3-9-4(b)：两篇文章的共有部分commonality.cloud(df,max.words=100,random.order=FALSE,color="#E7298A")