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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

**(Select all that apply.)** Which transformations are included with `tm`?

(Select all that apply.) Which transformations are included with <code>tm</code>?

**(Select all that apply.)** Which are typical stopwords?

(Select all that apply.) Which are typical stopwords?

The words "associated" and "clarify" will be removed from the list of stopwords, which will then be removed from `myText`.

`removeWords( )` will remove terms associated with English stopwords, then clarify (reduce) the results.

'myText' will subsequently contain stopwords, "associated", and "clarify."

English stopwords and the words "associated" and "clarify" will be removed from `myText`.

What is the result of the following command? 
`removeWords( myText, c(stopwords("english"), c("associated", "clarify")))`

What is the result of the following command?
<code>removeWords( myText, c(stopwords(&quot;english&quot;), c(&quot;associated&quot;, &quot;clarify&quot;)))</code>

Consider these two phrases:

"its common\nstock, and warrants"

vs. 

"its common stock, and warrants"

Which command will produce this change?

Consider these two phrases:
“its common\nstock, and warrants”
vs.
“its common stock, and warrants”
Which command will produce this change?

Preprocessing Text

Preprocessing Text

Quiz yourself on what you’ve learned about using tools to clean and preprocess text.

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 on Converting Text to Structured Data