Gain insights into NLP concepts using R, including the tm package, corpora, structured data conversion, and advanced search techniques. Discover quanteda and tidytext for text processing.

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This course will cover concepts in natural language processing (NLP). Developers will find this valuable because of the high demand for NLP skills. This includes understanding natural language when building applications like chatbots, sentiment analysis, search engines, and content recommendations. NLP also provides tools for data analysis, personalization, and content filtering.

In this course, you will learn concepts of NLP, how to use the tm package, the use of corpora, how to convert text to structured data, advanced search techniques, metadata, and how to use quanteda, and tidytext.

With this information, you can expect to command in-demand skills in many text-processing-related fields.

Performing Natural Language Processing with R

Terms (also known as tokens) are in rows. Document names are in columns.

Terms (also known as tokens) are in columns. Document names are in rows.

A collection of documents imported into one data structure.


A collection of documents imported into multiple data structures.


Which is the best definition of a document-term matrix?

Which is the best definition of a document-term matrix?

This creates a list of terms that appear less than 400 times.

This will create a list of the most frequent terms for each document.

This command creates a list of terms that appear a minimum of 400 times in `DTmatrix`.

`findFreqTerms(DTmatrix, lowfreq = 400)` trims `DTmatrix` to a list of 400 terms.

What will we get from the following command? `findFreqTerms(DTmatrix, lowfreq = 400)`

What will we get from the following command? <code>findFreqTerms(DTmatrix, lowfreq = 400)</code>

Words have meanings that depend on context.

Words may be similar but have modifiers that create different spellings.

Words can mean the same thing but have completely different spelling.

"Bag of words" can produce large datasets, which are difficult to manipulate.

What isn’t a problem with the "bag of words" strategy?

What isn’t a problem with the “bag of words” strategy?

`n = ` is the length of n-grams in tokens.

`width = ` is the maximum number of letters in the n-gram.

`width = ` is the minimum number of letters in the n-gram.

`width = ` is the minimum number of tokens in the resulting n-grams.

`ngrams( )` has two arguments. The first is the document to be broken into n-grams. What is the second?

<code>ngrams( )</code> has two arguments. The first is the document to be broken into n-grams. What is the second?

Which code is used to calculate `tf*idf`?

Which code is used to calculate <code>tf*idf</code>?

Creating a document-term matrix with `tm` calculates tf-idf as a default regardless of stopword removal.

The stopword removal makes tf-idf unnecessary.

tf*idf makes stop word removal unnecessary. 

The `tm` package automatically defines `stopwords = true` anytime `weighting = weightTfIdf`.

What is the role of `stopwords` when combined with `weighting = weightTfIdf`?

What is the role of <code>stopwords</code> when combined with <code>weighting = weightTfIdf</code>?

Natural Language Tools

Natural Language Tools

Test your knowledge of document search strategies, tokenization, n-grams, stemming, and tf-idf.

Before We Begin

Important Concepts in Natural Language Processing

Text Mining Package

Understanding Corpora and Sources

Converting Text to Structured Data

Document Insights and Advanced Search Techniques

Working with Metadata in the tm Package

Implementing NLP with the quanteda Package

Implementing NLP with the tidytext Package

Assess What You Have Learned About NLP

Concluding Remarks

Appendix

Quiz: Tokens, N-Grams, tf-idf, and Stemming