Complete Guide to NLP with R

Find out how to use the R programming language to implement natural language processing (NLP) algorithms.

Introduction

• Welcome to natural language processing with R
• Skills and tools you need to be successful in this course

1. Up and Running with tm

• What is tm and why do you need it?
• Real-world NLP with tm
• Real-world NLP with quanteda
• Real-world NLP with tidytext

2. Corpora and Sources

• Understanding corpora and sources
• Examining corpora
• Examining sources
• Custom sources
• Combining and subsetting corpora

3. Working with NLP Metadata

• Working with document metadata
• Make useful metadata
• Finding and filtering based on metadata

4. Preprocessing Text in Preparation for NLP

• Transformations
• Stop words
• Stemming
• Lemmatization
• Tokenization
• N-grams
• Part of speech tagging

5. Create Structured Data

• Understanding the document-term matrix
• Create the document-term matrix
• Weighting the document-term matrix
• Focus the document-term matrix

6. Apply Statistics to Text

• Word and document frequency
• Hierarchical clustering
• Associated terms

7. Sentiment Analysis

• What is sentiment analysis?
• Real-world example of sentiment analysis
• Sentiment datasets
• Sentiment tools

8. Visualizing Natural Language Processing

• Plotting text mining
• Plotting Zipf’s and Heap’s Law
• Word clouds

9. Conclusion

• Your next steps in NLP

10. Introduction to NLP Tidytext R

• Welcome to natural language processing with R
• Skills you need to be successful in this course

11. Use of Tidytext for NLP

• How to think like tidytext
• An example: Calculate the most popular terms in a document
• Tokenizing with unnest_tokens( )
• Stopwords, punctuation, whitespace, and numbers
• Stemming and lemmatization
• Term frequency with bind_tf_idf( )
• Sentiment analysis with sentiments( )
• Parts of speech with parts_of_speech( )
• Import and export from other NLP packages

12. Conclusion

• Next steps

13. Introduction to NLP with Quanteda R

• Welcome to natural language processing with R
• Skills and tools you need

14. Getting Started with Quanteda

• Introduction to quanteda
• Install quanteda

15. Understanding Corpora

• Create a quanteda corpus
• Create metadata with docvars
• Corpus subsets and groups
• Reshape and segment a corpus
• Remove lines from a corpus

16. Understanding Tokens

• Corpus and tokens
• Remove tokens and stopwords
• Group tokens
• Stemming with tokens

17. Understanding Document-Feature Matrix (DFM)

• Corpus, tokens, and DFM
• Create and modify a DFM
• Real-world analysis with DFM

18. Analysis and Visualization

• The quanteda textstats package
• Real-world text statistics with textstats
• Understand the quanteda sentiment package
• Real-world sentiment analysis with quanteda sentiment
• Visualization with textplots
• Use dplyr with quanteda

19. Conclusion

• Your next steps in NLP

20. Capstone Project

• Project introduction
• Project explanation