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.

Natural Language Processing with the R Programming Language.png

r_pack.tar.gz

Tm-exec-err

Tm-exec

Tm-exec-copy

Tm-exec-err-copy

R exec no errors

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

`quanteda` is an R package for quantitative text analysis.

`quanteda` is an NLP tool with frontends written in R, C, and Python.

`quanteda` follows an easy-to-understand syntax. Which function will be used with a corpus?

<code>quanteda</code> follows an easy-to-understand syntax. Which function will be used with a corpus?

Which command is used to convert a `data.frame` to a `quanteda` corpus?

Which command is used to convert a <code>data.frame</code> to a <code>quanteda</code> corpus?

What is the `textstat_valence()` function used for?


What is the <code>textstat_valence()</code> function used for?

`makeTermFrequencyInverseDocumentFrequency()`

Which `quanteda` function calculates tf-idf from a document-feature matrix?

Which <code>quanteda</code> function calculates tf-idf from a document-feature matrix?

Using `quanteda` for Natural Language Processing

Using <code>quanteda</code> for Natural Language Processing

Quiz yourself on what you’ve learned so far in this chapter. 


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: Implementing NLP with the quanteda Package