Complete Guide to NLP with R

سرفصل های دوره

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.

This course was created by Mark Niemann-Ross. We are pleased to host this training in our library.

01 - Introduction

01 - Welcome to natural language processing with R

02 - Skills and tools you need to be successful in this course

02 - 1. Up and Running with tm

01 - What is tm and why do you need it

02 - Real-world NLP with tm

03 - Real-world NLP with quanteda

04 - Real-world NLP with tidytext

03 - 2. Corpora and Sources

01 - Understanding corpora and sources

02 - Examining corpora

03 - Examining sources

04 - Custom sources

05 - Combining and subsetting corpora

04 - 3. Working with NLP Metadata

01 - Working with document metadata

02 - Make useful metadata

03 - Finding and filtering based on metadata

05 - 4. Preprocessing Text in Preparation for NLP

01 - Transformations

02 - Stop words

03 - Stemming

04 - Lemmatization

05 - Tokenization

06 - N-grams

07 - Part of speech tagging

06 - 5. Create Structured Data

01 - Understanding the document-term matrix

02 - Create the document-term matrix

03 - Weighting the document-term matrix

04 - Focus the document-term matrix

07 - 6. Apply Statistics to Text

01 - Word and document frequency

02 - Hierarchical clustering

03 - Associated terms

08 - 7. Sentiment Analysis

01 - What is sentiment analysis

02 - Real-world example of sentiment analysis

03 - Sentiment datasets

04 - Sentiment tools

09 - 8. Visualizing Natural Language Processing

01 - Plotting text mining

02 - Plotting Zipfs and Heaps Law

03 - Word clouds

10 - 9. Conclusion

01 - Your next steps in NLP

11 - 10. Introduction to NLP Tidytext R

01 - Welcome to natural language processing with R

02 - Skills you need to be successful in this course

12 - 11. Use of Tidytext for NLP

01 - How to think like tidytext

02 - An example Calculate the most popular terms in a document

03 - Tokenizing with unnest tokens( )

04 - Stopwords, punctuation, whitespace, and numbers

05 - Stemming and lemmatization

06 - Term frequency with bind tf idf( )

07 - Sentiment analysis with sentiments( )

08 - Parts of speech with parts of speech( )

09 - Import and export from other NLP packages

13 - 12. Conclusion

01 - Next steps

14 - 13. Introduction to NLP with Quanteda R

01 - Welcome to natural language processing with R

02 - Skills and tools you need

15 - 14. Getting Started with Quanteda

01 - Introduction to quanteda

02 - Install quanteda

16 - 15. Understanding Corpora

01 - Create a quanteda corpus

02 - Create metadata with docvars

03 - Corpus subsets and groups

04 - Reshape and segment a corpus

05 - Remove lines from a corpus

17 - 16. Understanding Tokens

01 - Corpus and tokens

02 - Remove tokens and stopwords

03 - Group tokens

04 - Stemming with tokens

18 - 17. Understanding Document-Feature Matrix (DFM)

01 - Corpus, tokens, and DFM

02 - Create and modify a DFM

03 - Real-world analysis with DFM

19 - 18. Analysis and Visualization

01 - The quanteda textstats package

02 - Real-world text statistics with textstats

03 - Understand the quanteda sentiment package

04 - Real-world sentiment analysis with quanteda sentiment

05 - Visualization with textplots

06 - Use dplyr with quanteda

20 - 19. Conclusion

01 - Your next steps in NLP

21 - 20. Capstone Project

01 - Project introduction

02 - Project explanation

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تولید کننده: LinkedIn Learning (Lynda)