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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

A parenthetical group in a regular expression

What is a typical part of speech?

Sentiment analysis uses artificial intelligence to read and rank a document.

Sentiment analysis assigns an arbitrary number to a document and then confirms the value against a known object.

Sentiment analysis is calculated using euclidean distances.

Sentiment analysis is the result of the values of a dictionary of words and related values.

How is sentiment analysis calculated?

In sentiment analysis, what does the function take into account to understand the text's actual meaning?

In sentiment analysis, what does the function take into account to understand the text’s actual meaning?

What does the tf-idf measure in a document relative to its frequency in a corpus of documents?

What does the tf-idf measure in a document relative to its frequency in a corpus of documents?

A description of tools and frameworks for NLP

Documentation for data visualization techniques

What does the CRAN task view for natural language processing provide to the users?


What does the CRAN task view for natural language processing provide to the users?

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

End of Chapter Quiz