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Text mining methods allow us to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud, which is a visual representation of text data.

The procedure of creating word clouds is very simple in R if you know the different steps to execute. The text mining package (tm) and the word cloud generator package (wordcloud) are available in R for helping us to analyze texts and to quickly visualize the keywords as a word cloud.

In this article, we’ll describe, step by step, how to generate word clouds using the R software.

## 3 reasons you should use word clouds to present your text data

1. Word clouds add simplicity and clarity. The most used keywords stand out better in a word cloud
2. Word clouds are a potent communication tool. They are easy to understand, to be shared and are impactful
3. Word clouds are visually engaging than a table data

## Who is using word clouds ?

• Researchers : for reporting qualitative data
• Marketers : for highlighting the needs and pain points of customers
• Educators : to support essential issues
• Politicians and journalists
• social media sites : to collect, analyze and share user sentiments

## The 5 main steps to create word clouds in R

### Step 1: Create a text file

In the following examples, I’ll process the “I have a dream speech” from “Martin Luther King” but you can use any text you want :

• Copy and paste the text in a plain text file (e.g : ml.txt)
• Save the file

Note that, the text should be saved in a plain text (.txt) file format using your favorite text editor.

### Step 2 : Install and load the required packages

Type the R code below, to install and load the required packages:

# Install
install.packages("tm")  # for text mining
install.packages("SnowballC") # for text stemming
install.packages("wordcloud") # word-cloud generator
install.packages("RColorBrewer") # color palettes
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")

### Step 3 : Text mining

The text is loaded using Corpus() function from text mining ™ package. Corpus is a list of a document (in our case, we only have one document).

1. We start by importing the text file created in Step 1

To import the file saved locally in your computer, type the following R code. You will be asked to choose the text file interactively.

In the example below, I’ll load a .txt file hosted on STHDA website:

# Read the text file from internet
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
1. Load the data as a corpus
# Load the data as a corpus
docs <- Corpus(VectorSource(text))

VectorSource() function creates a corpus of character vectors

1. Inspect the content of the document
inspect(docs)

#### Text transformation

Transformation is performed using tm_map() function to replace, for example, special characters from the text.

Replacing “/”, “@” and “|” with space:

toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

#### Cleaning the text

the tm_map() function is used to remove unnecessary white space, to convert the text to lower case, to remove common stopwords like ‘the’, “we”.

The information value of ‘stopwords’ is near zero due to the fact that they are so common in a language. Removing this kind of words is useful before further analyses. For ‘stopwords’, supported languages are danish, dutch, english, finnish, french, german, hungarian, italian, norwegian, portuguese, russian, spanish and swedish. Language names are case sensitive.

I’ll also show you how to make your own list of stopwords to remove from the text.

You could also remove numbers and punctuation with removeNumbers and removePunctuation arguments.

Another important preprocessing step is to make a text stemming which reduces words to their root form. In other words, this process removes suffixes from words to make it simple and to get the common origin. For example, a stemming process reduces the words “moving”, “moved” and “movement” to the root word, “move”.

Note that, text stemming require the package ‘SnowballC’.

The R code below can be used to clean your text :

# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2"))
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)

### Step 4 : Build a term-document matrix

Document matrix is a table containing the frequency of the words. Column names are words and row names are documents. The function TermDocumentMatrix() from text mining package can be used as follow :

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
word freq
will         will   17
freedom   freedom   13
ring         ring   12
day           day   11
dream       dream   11
let           let   11
every       every    9
able         able    8
one           one    8
together together    7

### Step 5 : Generate the Word cloud

The importance of words can be illustrated as a word cloud as follow :

set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))

The above word cloud clearly shows that “Will”, “freedom”, “dream”, “day” and “together” are the five most important words in the “I have a dream speech” from Martin Luther King.

Arguments of the word cloud generator function :

• words : the words to be plotted
• freq : their frequencies
• min.freq : words with frequency below min.freq will not be plotted
• max.words : maximum number of words to be plotted
• random.order : plot words in random order. If false, they will be plotted in decreasing frequency
• rot.per : proportion words with 90 degree rotation (vertical text)
• colors : color words from least to most frequent. Use, for example, colors =“black” for single color.

## Go further

### Explore frequent terms and their associations

You can have a look at the frequent terms in the term-document matrix as follow. In the example below we want to find words that occur at least four times :

findFreqTerms(dtm, lowfreq = 4)
[1] "able"     "day"      "dream"    "every"    "faith"    "free"     "freedom"  "let"      "mountain" "nation"
[11] "one"      "ring"     "shall"    "together" "will"

You can analyze the association between frequent terms (i.e., terms which correlate) using findAssocs() function. The R code below identifies which words are associated with “freedom” in I have a dream speech :

findAssocs(dtm, terms = "freedom", corlimit = 0.3)
$freedom let ring mississippi mountainside stone every mountain state 0.89 0.86 0.34 0.34 0.34 0.32 0.32 0.32 ### The frequency table of words head(d, 10) word freq will will 17 freedom freedom 13 ring ring 12 day day 11 dream dream 11 let let 11 every every 9 able able 8 one one 8 together together 7 ### Plot word frequencies The frequency of the first 10 frequent words are plotted : barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]\$word,
col ="lightblue", main ="Most frequent words",
ylab = "Word frequencies")

# Infos

This analysis has been performed using R (ver. 3.3.2).