TweetsDS.Clean<-tm_map(tweetsDS.Clean,removePunctuation) TweetsDS.Clean<-tm_map(tweetsDS.Clean,removeWords,stopwords("english")) TweetsDS.Clean<-tm_map(tweetsDS.Clean,removeNumbers) TweetsDS.Clean<-tm_map(tweetsDS.Corpus,tolower) TweetsDS.Clean<-tm_map(tweetsDS.Corpus, PlainTextDocument This includes removing stopwords, numbers, whitespace, etc. and converting the corpus into a plain text document. TweetsDS.Corpus<-Corpus(VectorSource(tweetsDS$content)) Now, we need to create the corpus of data. Install.packages("RColorBrewer") # color palettes Install.packages("wordcloud") # word-cloud generator Install.packages("SnowballC") # for text stemming This technique is sometimes referred to as text clouds or tag clouds, which is a visual representation of text data.įirst, we need to load the CSV data and then load the required library for building the word cloud. Word cloud is a text mining technique that allows us to highlight the most frequently used keywords in paragraphs of text. In this article, we are going to see how to build a word cloud with R.
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