Complete Guide to NLP with R

Course details

Natural Language Processing is to words as Computer Vision is to pictures! Learn NLP with the R programming language. In this course, experienced technologist Mark Niemann-Ross 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. Learn about frameworks that you can use with NLP, as well as the importance of corpora and sources. Find out how to work with NLP metadata and preprocess text in preparation for NLP. Explore creating structured data, applying statistics to text, and performing sentiment analysis, and then dive into visualizing NLP. Discover ways to use tidytext and quanteda R for NLP. Build your understanding of corpora, tokens, and document-feature matrix (DFM). Plus, go over analysis and visualization.

Concepts

0. Introduction

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

1. Up and Running with tm

03 - What is tm and why do you need it
04 - Real-world NLP with tm
05 - Real-world NLP with quanteda
06 - Real-world NLP with tidytext

2. Corpora and Sources

07 - Understanding corpora and sources
08 - Examining corpora
09 - Examining sources
10 - Custom sources
11 - Combining and subsetting corpora

3. Working with NLP Metadata

12 - Working with document metadata
13 - Make useful metadata
14 - Finding and filtering based on metadata

4. Preprocessing Text in Preparation for NLP

15 - Transformations
16 - Stop words
17 - Stemming
18 - Lemmatization
19 - Tokenization
20 - N-grams
21 - Part of speech tagging

5. Create Structured Data

22 - Understanding the document-term matrix
23 - Create the document-term matrix
24 - Weighting the document-term matrix
25 - Focus the document-term matrix

6. Apply Statistics to Text

26 - Word and document frequency
27 - Hierarchical clustering
28 - Associated terms

7. Sentiment Analysis

29 - What is sentiment analysis
30 - Real-world example of sentiment analysis
31 - Sentiment datasets
32 - Sentiment tools

8. Visualizing Natural Language Processing

33 - Plotting text mining
34 - Plotting Zipf s and Heap s Law
35 - Word clouds

9. Conclusion

36 - Your next steps in NLP

10. Introduction to NLP Tidytext R

37 - Welcome to natural language processing with R
38 - Skills you need to be successful in this course

11. Use of Tidytext for NLP

39 - How to think like tidytext
40 - An example - Calculate the most popular terms in a document
41 - Tokenizing with unnest tokens( )
42 - Stopwords, punctuation, whitespace, and numbers
43 - Stemming and lemmatization
44 - Term frequency with bind tf idf( )
45 - Sentiment analysis with sentiments( )
46 - Parts of speech with parts of speech( )
47 - Import and export from other NLP packages

12. Conclusion

48 - Next steps

13. Introduction to NLP with Quanteda R

49 - Welcome to natural language processing with R
50 - Skills and tools you need

14. Getting Started with Quanteda

51 - Introduction to quanteda
52 - Install quanteda

15. Understanding Corpora

53 - Create a quanteda corpus
54 - Create metadata with docvars
55 - Corpus subsets and groups
56 - Reshape and segment a corpus
57 - Remove lines from a corpus

16. Understanding Tokens

58 - Corpus and tokens
59 - Remove tokens and stopwords
60 - Group tokens
61 - Stemming with tokens

17. Understanding Document-Feature Matrix (DFM)

62 - Corpus, tokens, and DFM
63 - Create and modify a DFM
64 - Real-world analysis with DFM

18. Analysis and Visualization

65 - The quanteda textstats package
66 - Real-world text statistics with textstats
67 - Understand the quanteda sentiment package
68 - Real-world sentiment analysis with quanteda sentiment
69 - Visualization with textplots
70 - Use dplyr with quanteda

19. Conclusion

71 - Your next steps in NLP

20. Capstone Project

72 - Project introduction
73 - Project explanation