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Performing Natural Language Processing with R

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.

50 Lessons

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LEARNING OBJECTIVES

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

Learning Roadmap

50 Lessons8 Quizzes1 Assessment2 Challenges

Before We Begin

Get familiar with natural language processing using R, focusing on practical application and foundational skills.

Introduction to the Course

Key Skills for Success

Important Concepts in Natural Language Processing

Understand crucial NLP concepts like tokenization, sentiment analysis, tf-idf, and R packages.

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

Break apart the tm package in R for effective text mining and NLP tasks.

Understanding Corpora and Sources

4 Lessons

Enhance your skills in understanding corpora, corpus classes, and source types in R.

Converting Text to Structured Data

4 Lessons

Map out the steps for converting and cleaning text for structured data analysis.

Document Insights and Advanced Search Techniques

8 Lessons

Focus on R techniques for tokenization, stemming, DTMs, tf-idf, n-grams, and visualization.

Working with Metadata in the tm Package

4 Lessons

Learn how to improve metadata management in text analysis using the R 'tm' package.

Implementing NLP with the quanteda Package

7 Lessons

Learn how to use the quanteda package for advanced text analysis and sentiment evaluation.

Implementing NLP with the tidytext Package

7 Lessons

Get started with text mining in R using the tidytext package for comprehensive NLP tasks.

Assess What You Have Learned About NLP

Assessment

10.

Concluding Remarks

Examine your achievements in text mining, sentiment analysis, and NLP with R.

Conclusion

11.

Appendix

Find out about sample file sources and key public domain novels used in the course.

A Note About the Sample Files

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Mark Niemann-Ross

I teach about the R programming language, SQL, and Raspberry Pi. When I'm not being serious, I write science fiction and play in boats.

Learn more about Mark

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