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All LessonsFree Lessons (6)

Before We Begin

Introduction to the Course Key Skills for Success

Important Concepts in Natural Language Processing

Understanding Tokens and N-Grams Creating a List of Frequent Terms Removing Unnecessary Terms Reducing and Aggregating Terms Mid-Chapter Quiz Determining the Sentiment of a Document Understanding Parts of Speech Searching for Matching Documents with tf-idf Comparing R Packages for NLP End of Chapter Quiz

Text Mining Package

Installation and Documentation Using the File Folder as Corpus

Understanding Corpora and Sources

Corpus Classes and Source Types Using a Suitable Corpus Class Using a Suitable Source Type Quiz: Understanding Corpora and Sources

Converting Text to Structured Data

Document-Term Matrix Stopword Removal Removing Other Unnecessary Terms Quiz on Converting Text to Structured Data

Document Insights and Advanced Search Techniques

Alternative Approaches Understanding Tokenization and Its Importance Preserve Phrases with N-grams Analyzing Textual Comparisons with Document-Term Matrices Refining Textual Meaning through Stemming Implement tf-idf with tm Visualization with tm Quiz: Tokens, N-Grams, tf-idf, and Stemming

Working with Metadata in the tm Package

Understanding Metadata in Text Analysis Assigning Metadata Using Metadata Quiz: Working with Metadata in the tm Package

Implementing NLP with the quanteda Package

Understanding quanteda Installing quanteda quanteda Basics Implementing Sentiment Analysis with quanteda Text Statistics with quanteda Calculate tf-idf with quanteda Quiz: Implementing NLP with the quanteda Package

Implementing NLP with the tidytext Package

Understanding tidytext

tidytext Basics

Stemming with tidytext

Lemmatization with tidytext

Calculate tf-idf with tidytext

Recognizing Parts of Speech with tidytext

Quiz on tidytext

Course Assessment

Assess What You Have Learned About NLP

Concluding Remarks

Appendix

A Note About the Sample Files

Performing Natural Language Processing with R/

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Quiz on tidytext

Quiz on tidytext

Quiz yourself on tidytext concepts.

We'll cover the following...

tidytext Concepts

1.

How does tidytext recommend removing stopwords?

A.

remove_stop_words(by = "word") %>%

B.

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

C.

filter(stop_words, word) %>%

D.

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

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