Text Mining in R

Text mining is the process of extracting useful information from unstructured text data such as documents, reviews, or messages.

Unlike numeric data, text data must be cleaned and transformed before analysis.

What Is Text Data?

Text data includes words, sentences, and paragraphs written in natural language.

Examples include customer reviews, emails, articles, and social media posts.

Why Text Mining Is Important

Large amounts of valuable information exist in text form.

Text mining helps convert this information into structured insights.

• Sentiment analysis
• Topic extraction
• Keyword analysis
• Document classification

Common Steps in Text Mining

Text mining usually follows a structured workflow.

• Text collection
• Text cleaning
• Tokenization
• Analysis and modeling

Key R Packages for Text Mining

R provides powerful libraries for text analysis.

• tm – text mining framework
• stringr – string handling
• tidytext – tidy text analysis

Installing Required Packages

Install text mining packages before working with text data.

install.packages("tm")
install.packages("tidytext")

Creating a Text Corpus

A corpus is a collection of text documents.

It is the starting point for most text mining tasks.

library(tm)
text_data <- Corpus(VectorSource(c("R is powerful", "Text mining with R")))

Cleaning Text Data

Cleaning removes noise and improves analysis quality.

Common cleaning steps include removing punctuation and extra spaces.

text_data <- tm_map(text_data, content_transformer(tolower))
text_data <- tm_map(text_data, removePunctuation)
text_data <- tm_map(text_data, stripWhitespace)

Tokenization

Tokenization splits text into individual words or terms.

These tokens form the basis of text analysis.

library(tidytext)
tokens <- tidy(text_data)

Term Frequency

Term frequency shows how often words appear in text.

This helps identify important or commonly used terms.

Applications of Text Mining

• Customer feedback analysis
• Spam detection
• Search engine indexing
• Document summarization

📝 Practice Exercises

Exercise 1

Explain what text mining means.

Exercise 2

Create a text corpus in R.

Exercise 3

Clean text by converting it to lowercase.

Exercise 4

Tokenize text data.

✅ Practice Answers

Answer 1

Text mining extracts useful information from unstructured text data.

Answer 2

Corpus(VectorSource(c("Sample text")))

Answer 3

tm_map(text_data, content_transformer(tolower))

Answer 4

tidy(text_data)

What’s Next?

In the next lesson, you will learn about Reporting and how R helps create professional reports.