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

N-grams reduce the size of DTMs or corpora.

N-grams can be used to identify popular phrases.

N-grams can be used to identify missing data.

N-grams can be used to identify redundant tokens.

What is the practical use of n-grams?

What is the most common result of applying a tokenizer to the string `Visualize World Peace`?

What is the most common result of applying a tokenizer to the string <code>Visualize World Peace</code>?

The probability of term appearance in a document.

A link to the location of a term in a document.

What values are stored in the cells of a typical document-term matrix?

What values are stored in the cells of a typical document-term matrix?

Stopwords typically indicate the end of a sentence, phrase, or paragraph.

Stopwords provide grammatical and linguistic flow, although their importance can vary depending on the context.

Stopwords are objectionable words banned from media.

Stopwords always appear just prior to a period.

Which is an accurate description of stopwords in NLP?

Which is an accurate description of stopwords in NLP?

Which option isn’t a typical transformation in NLP?

Which option isn’t a typical transformation in NLP?

What is the most likely result of `stemDocument(“cheaply”)`?

What is the most likely result of <code>stemDocument(“cheaply”)</code>?

What is the most likely result of `lemmatize_strings("cheaply", make_lemma_dictionary(myText, engine = 'hunspell'))`?

What is the most likely result of <code>lemmatize_strings(&quot;cheaply&quot;, make_lemma_dictionary(myText, engine = 'hunspell'))</code>?

Tokens, N-grams, Frequent Terms, Cleaning, and Reducing Terms

Tokens, N-grams, Frequent Terms, Cleaning, and Reducing Terms

Quiz yourself on what you’ve learned so far about n-grams, tokens, term frequency, stopwords, stemming, and lemmatization. 

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

Mid-Chapter Quiz