load the text

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.

text < - readLines(file.choose())

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"
text < - readLines(filePath)

2. Load the data as a corpus

# Load the data as a corpus
docs < - Corpus(VectorSource(text))

VectorSource() function creates a corpus of character vectors

3. 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)

word cloud and text mining, I have a dream speech from Martin Luther King

• 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.

word cloud and text mining