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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

`tidytext` is an R package designed to correct spelling and grammar.

`tidytext` is an R package designed for natural language processing using `tidyverse` concepts.

`tidytext` is an R package based on Python NLP programming concepts.

`tidytext` is a general NLP framework used for academic research.

Which statement is accurate about `tidytext`?

Which statement is accurate about <code>tidytext</code>?

Which of these NLP tasks is *not* addressed by `tidytext`?

Which of these NLP tasks is not addressed by <code>tidytext</code>?

`weighting = function(x)
   weightTfIdf(x, normalize = TRUE))
)`

Which command does `tidytext` supply for tf-idf?

Which command does <code>tidytext</code> supply for tf-idf?

 `anti_join(stop_words, by = "word") %>%`


`tokens_remove(pattern = stopwords("english")) %>%`


How does `tidytext` recommend removing stopwords? 

How does <code>tidytext</code> recommend removing stopwords?

A list of available types of the `parts_of_speech` data frame.

A list of available words of the `parts_of_speech` data frame.

The text with unique POS terms attached to each word.

The unique tokens in a text with terms attached to each word.

What will `unique(parts_of_speech$pos)` produce?

What will <code>unique(parts_of_speech$pos)</code> produce?

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 tidytext