Supervised machine learning for text analysis in R
著者
書誌事項
Supervised machine learning for text analysis in R
（Chapman & Hall/CRC data science series）
CRC Press, 2022
- : pbk
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注記
Includes bibliographical references (p. 369-378) and index
内容説明・目次
内容説明
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
目次
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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