用R生成wordcloud—— 来自于twitteR 项目

下面的例子来自于twitteR，用R从twitter中挖掘信息， 其实中文也有Rweibo，有时间可以研究一下。

https://sites.google.com/site/miningtwitter/questions/talking-about/wordclouds 

共有三篇详细介绍，可能需要越过GFW

下面是第一篇的例子：

https://sites.google.com/site/miningtwitter/questions/talking-about/wordclouds/wordcloud1

Simple Wordcloud    wordclouds

 

A wordcloud can be one of the best tools that allows us to visualize most of the words and terms contained in tweets. Although its main use is for exploratory purposes, they have the advantage to be understandable by most users, and to be visually attractive to the human eyes (if done adequately).   How to create a wordcloud? Wordclouds are relatively simple to make. Here's the main recipe steps Download some tweets (via twitteR or XML) Extract the text from the tweets Do some text manipulation (for cleaning & formatting) Create a lexical Corpus and a TermDocumentMatrix (via tm) Obtain words and their frequencies Plot the wordcloud     Example 1: tweets via twitteR Step 1: Load all the required packages   library(twitteR) library(tm) library(wordcloud) library(RColorBrewer)   Step 2: Let's get some tweets in english containing the words "machine learning" mach_tweets = searchTwitter("machine learning", n=500, lang="en")   Step 3: Extract the text from the tweets in a vector mach_text = sapply(mach_tweets, function(x) x$getText())   Step 4: Construct the lexical Corpus and the Term Document Matrix We use the function  Corpus to create the corpus, and the function  VectorSource to indicate that the text is in the character vector  mach_text. In order to create the term-document matrix we apply different transformation such as removing numbers, punctuation symbols, lower case, etc.   # create a corpus mach_corpus = Corpus(VectorSource(mach_text)) # create document term matrix applying some transformations tdm = TermDocumentMatrix(mach_corpus, control = list(removePunctuation = TRUE, stopwords = c("machine", "learning", stopwords("english")), removeNumbers = TRUE, tolower = TRUE))   Step 5: Obtain words and their frequencies   # define tdm as matrix m = as.matrix(tdm) # get word counts in decreasing order word_freqs = sort(rowSums(m), decreasing=TRUE) # create a data frame with words and their frequencies dm = data.frame(word=names(word_freqs), freq=word_freqs) Step 6: Let's plot the wordcloud   # plot wordcloud wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, "Dark2")) # save the image in png format png("MachineLearningCloud.png", width=12, height=8, units="in", res=300) wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, "Dark2")) dev.off()