Supervised machine learning for text analysis in R

Author(s)

Bibliographic Information

Supervised machine learning for text analysis in R

Emil Hvitfeldt, Julia Silge

(Chapman & Hall/CRC data science series)(A Chapman & Hall book)

CRC Press, 2022

1st ed

  • : hbk

Available at  / 2 libraries

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Note

Includes bibliographical references (p. 369-378) and index

Description and Table of Contents

Description

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models? Build beginning-to-end workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data

Table of Contents

1. Language and modeling. 2. Tokenization. 3. Stop words. 4. Stemming. 5. Word Embeddings. 6. Regression. 7. Classification. 8. Dense neural networks. 9. Long short-term memory (LSTM) networks. 10. Convolutional neural networks.

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