长沙协会网站设计专业服务_企业网站托管后果_社区推广方法有哪些_seo如何优化关键词排名

Gonewind词云图

library(tm)library(wordcloud)article <- tolower(readLines('D:/RWorkPlace/第3章_类别比较型图表/gonewind.txt', warn = FALSE))article <- Corpus(VectorSource(article))# 去除标点article <- tm_map(article, content_transformer(tolower))article <- tm_map(article, removePunctuation)article <- tm_map(article, removeNumbers)article <- tm_map(article, removeWords, stopwords("en"))  # 去除英文停用词article <- tm_map(article, stripWhitespace)wordcloud(words = article, min.freq = 5, scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))

Sanguo1词云图

library(tm)library(wordcloud)library(jiebaR)# 读取中文文本文件text <- readLines("D:/RWorkPlace/第3章_类别比较型图表/sanguo1.txt", encoding = "UTF-8")# 将文本合并为一个字符串text <- paste(text, collapse = " ")# 使用jiebaR分词engine<-worker()segmented_text <- segment(text,engine)# 创建语料库corpus <- Corpus(VectorSource(segmented_text))# 进行文本预处理corpus <- tm_map(corpus, removePunctuation)corpus <- tm_map(corpus, removeNumbers)corpus <- tm_map(corpus, stripWhitespace)# 将Corpus对象转换为数据框corpus_df <- data.frame(text = sapply(corpus, as.character))# 创建词云图wordcloud(words = corpus_df$text, min.freq = 50, scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))

NEWS词云图

library(tm)library(wordcloud)article <- tolower(readLines('D:/RWorkPlace/第3章_类别比较型图表/NEWS.txt', warn = FALSE))article <- Corpus(VectorSource(article))# 去除标点article <- tm_map(article, content_transformer(tolower))article <- tm_map(article, removePunctuation)article <- tm_map(article, removeNumbers)article <- tm_map(article, removeWords, stopwords("en"))  # 去除英文停用词article <- tm_map(article, stripWhitespace)wordcloud(words = article, min.freq = 10, scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))

全唐诗词云图

library(tm)library(wordcloud)library(RColorBrewer)library(openxlsx)library(jiebaR)# 导入xlsx文件file_path <- "D:/RWorkPlace/第3章_类别比较型图表/全唐诗.xlsx"data <- read.xlsx(file_path, detectDates = TRUE)# 合并多列文本为一个文本向量text <- paste(data$poet, data$title, data$poem, sep = " ")# 使用jiebaR分词engine<-worker()segmented_text <- segment(text,engine)# 创建语料库corpus <- Corpus(VectorSource(segmented_text))# 进行文本预处理corpus <- tm_map(corpus, removePunctuation)corpus <- tm_map(corpus, removeNumbers)corpus <- tm_map(corpus, stripWhitespace)# 将Corpus对象转换为数据框corpus_df <- data.frame(text = sapply(corpus, as.character))# 创建词云图wordcloud(words = corpus_df$text, min.freq = 70, scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))