Introduction to NLP Using R

Introduction

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

1. Up and Running with tm

• What is tm and why do you need it?
• tm documentation walk-through
• 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
• Ngrams
• 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

Conclusion

• Your next steps in NLP