Supervised Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series)

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
I. Natural Language Features
	1. Language and modeling
		1.1. Linguistics for text analysis
		1.2. A glimpse into one area: morphology
		1.3. Different languages
		1.4. Other ways text can vary
		1.5. Summary
			1.5.1. In this chapter, you learned:
	2. Tokenization
		2.1. What is a token?
		2.2. Types of tokens
			2.2.1. Character tokens
			2.2.2. Word tokens
			2.2.3. Tokenizing by n-grams
			2.2.4. Lines, sentence, and paragraph tokens
		2.3. Where does tokenization break down?
		2.4. Building your own tokenizer
			2.4.1. Tokenize to characters, only keeping letters
			2.4.2. Allow for hyphenated words
			2.4.3. Wrapping it in a function
		2.5. Tokenization for non-Latin alphabets
		2.6. Tokenization benchmark
		2.7. Summary
			2.7.1. In this chapter, you learned:
	3. Stop words
		3.1. Using premade stop word lists
			3.1.1. Stop word removal in R
		3.2. Creating your own stop words list
		3.3. All stop word lists are context-specific
		3.4. What happens when you remove stop words
		3.5. Stop words in languages other than English
		3.6. Summary
			3.6.1. In this chapter, you learned:
	4. Stemming
		4.1. How to stem text in R
		4.2. Should you use stemming at all?
		4.3. Understand a stemming algorithm
		4.4. Handling punctuation when stemming
		4.5. Compare some stemming options
		4.6. Lemmatization and stemming
		4.7. Stemming and stop words
		4.8. Summary
			4.8.1. In this chapter, you learned:
	5. Word Embeddings
		5.1. Motivating embeddings for sparse, high-dimensional
		5.2. Understand word embeddings by finding them yourself
		5.3. Exploring CFPB word embeddings
		5.4. Use pre-trained word embeddings
		5.5. Fairness and word embeddings
		5.6. Using word embeddings in the real world
		5.7. Summary
			5.7.1. In this chapter, you learned:
II. Machine Learning Methods
	Overview
	6. Regression
		6.1. A first regression model
			6.1.1. Building our first regression model
			6.1.2. Evaluation
		6.2. Compare to the null model
		6.3. Compare to a random forest model
		6.4. Case study: removing stop words
		6.5. Case study: varying n-grams
		6.6. Case study: lemmatization
		6.7. Case study: feature hashing
			6.7.1. Text normalization
		6.8. What evaluation metrics are appropriate?
		6.9. The full game: regression
			6.9.1. Preprocess the data
			6.9.2. Specify the model
			6.9.3. Tune the model
			6.9.4. Evaluate the modeling
		6.10. Summary
			6.10.1. In this chapter, you learned:
	7. Classification
		7.1. A first classification model
			7.1.1. Building our first classification model
			7.1.2. Evaluation
		7.2. Compare to the null model
		7.3. Compare to a lasso classification model
		7.4. Tuning lasso hyperparameters
		7.5. Case study: sparse encoding
		7.6. Two-class or multiclass?
		7.7. Case study: including non-text data
		7.8. Case study: data censoring
		7.9. Case study: custom features
			7.9.1. Detect credit cards
			7.9.2. Calculate percentage censoring
			7.9.3. Detect monetary amounts
		7.10. What evaluation metrics are appropriate?
		7.11. The full game: classification
			7.11.1. Feature selection
			7.11.2. Specify the model
			7.11.3. Evaluate the modeling
		7.12. Summary
			7.12.1. In this chapter, you learned:
III. Deep Learning Methods
	Overview
	8. Dense neural networks
		8.1. Kickstarter data
		8.2. A first deep learning model
			8.2.1. Preprocessing for deep learning
			8.2.2. One-hot sequence embedding of text
			8.2.3. Simple flattened dense network
			8.2.4. Evaluation
		8.3. Using bag-of-words features
		8.4. Using pre-trained word embeddings
		8.5. Cross-validation for deep learning models
		8.6. Compare and evaluate DNN models
		8.7. Limitations of deep learning
		8.8. Summary
			8.8.1. In this chapter, you learned:
	9. Long short-term memory (LSTM) networks
		9.1. A first LSTM model
			9.1.1. Building an LSTM
			9.1.2. Evaluation
		9.2. Compare to a recurrent neural network
		9.3. Case study: bidirectional LSTM
		9.4. Case study: stacking LSTM layers
		9.5. Case study: padding
		9.6. Case study: training a regression model
		9.7. Case study: vocabulary size
		9.8. The full game: LSTM
			9.8.1. Preprocess the data
			9.8.2. Specify the model
		9.9. Summary
			9.9.1. In this chapter, you learned:
	10. Convolutional neural networks
		10.1. What are CNNs?
			10.1.1. Kernel
			10.1.2. Kernel size
		10.2. A first CNN model
		10.3. Case study: adding more layers
		10.4. Case study: byte pair encoding
		10.5. Case study: explainability with LIME
		10.6. Case study: hyperparameter search
		10.7. Cross-validation for evaluation
		10.8. The full game: CNN
			10.8.1. Preprocess the data
			10.8.2. Specify the model
		10.9. Summary
			10.9.1. In this chapter, you learned:
IV. Conclusion
	Text models in the real world
	Appendix
		A. Regular expressions
			A.1. Literal characters
				A.1.1. Meta characters
			A.2. Full stop, the wildcard
			A.3. Character classes
				A.3.1. Shorthand character classes
			A.4. Quantifiers
			A.5. Anchors
			A.6. Additional resources
		B. Data
			B.1. Hans Christian Andersen fairy tales
			B.2. Opinions of the Supreme Court of the United States
			B.3. Consumer Financial Protection Bureau (CFPB) complaints
			B.4. Kickstarter campaign blurbs
		C. Baseline linear classifier
			C.1. Read in the data
			C.2. Split into test/train and create resampling folds
			C.3. Recipe for data preprocessing
			C.4. Lasso regularized classification model
			C.5. A model workflow
			C.6. Tune the workflow
References
Index