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Performing Natural Language Processing with R

Intermediate

50 Lessons

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

1 Assessment

100 Playgrounds

8 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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...Show More

What You'll Learn

Working knowledge of natural language processing, including sentiment analysis, statistical analysis of corpus contents, and use of metadata

Hands-on experience with R natural language processing packages, including tidytext, tm, and quanteda

Familiarity with term frequency, stemming, n-grams, and lemmatization

Understanding of parts of speech and ability to apply it to natural language research

The ability to use tf-idf to identify documents corresponding with terms

Working knowledge of natural language processing, including sentiment analysis, statistical analysis of corpus contents, and use of metadata

Course Content

Before We Begin

2 Lessons

Introduction to the Course

Key Skills for Success

Important Concepts in Natural Language Processing

10 Lessons

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

2 Lessons

Installation and Documentation

Using the File Folder as Corpus

Understanding Corpora and Sources

4 Lessons

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

4 Lessons

Document-Term Matrix

Stopword Removal

Removing Other Unnecessary Terms

Quiz on Converting Text to Structured Data

Document Insights and Advanced Search Techniques

8 Lessons

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

4 Lessons

Understanding Metadata in Text Analysis

Assigning Metadata

Using Metadata

Quiz: Working with Metadata in the tm Package

Implementing NLP with the quanteda Package

7 Lessons

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

7 Lessons

Understanding tidytext

tidytext Basics

Stemming with tidytext

Lemmatization with tidytext

Calculate tf-idf with tidytext

Recognizing Parts of Speech with tidytext

Quiz on tidytext

Assess What You Have Learned About NLP

Assessment

11.

Concluding Remarks

1 Lessons

Conclusion

12.

Appendix

1 Lessons

A Note About the Sample Files

Course Author

Mark Niemann-Ross

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