NLP with Quanteda R
1h 38mAdvanced2023-03-21
Authors

Mark Niemann-Ross
Technologist experienced in hardware, software, and science fiction
Course details
Natural language processing is to words as computer vision is to pictures. In this course, expert technologist Mark Niemann-Ross gets you started learning NLP with the R programming language. Mark shows you how to use the R programming language to implement natural language processing algorithms. R is uniquely adept at manipulating matrices and producing statistics, both of which are core to NLP. Mark steps you through using quanteda, an alternative text mining framework, and helps you understand what corpora are and how you can create, modify, and use them. He explains tokens and a document-feature matrix (DFM). Plus, Mark explores analysis and visualization via the quanteda textstat package, the sentiment package, textplot, and dplyr.
Skills covered
RStatisticsNatural Language Processing (NLP)AdvancedArtificial Intelligence (AI)Programming LanguagesData ScienceOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Welcome to natural language processing with R
- 02 - Skills and tools you need
1. Getting Started with Quanteda
- 03 - Introduction to quanteda
- 04 - Install quanteda
2. Understanding Corpora
- 05 - Create a quanteda corpus
- 06 - Create metadata with docvars
- 07 - Corpus subsets and groups
- 08 - Reshape and segment a corpus
- 09 - Remove lines from a corpus
3. Understanding Tokens
- 10 - Corpus and tokens
- 11 - Remove tokens and stopwords
- 12 - Group tokens
- 13 - Stemming with tokens
4. Understanding Document-Feature Matrix (DFM)
- 14 - Corpus, tokens, and DFM
- 15 - Create and modify a DFM
- 16 - Real-world analysis with DFM
5. Analysis and Visualization
- 17 - The quanteda textstat package
- 18 - Real-world text statistics with textstat
- 19 - Understand the quanteda sentiment package
- 20 - Real-world sentiment analysis with quanteda sentiment
- 21 - Visualization with textplot
- 22 - Use dplyr with quanteda
Conclusion
- 23 - Your next steps in NLP