1st Edition

Supervised Machine Learning for Text Analysis in R





  • Available for pre-order. Item will ship after November 4, 2021
ISBN 9780367554194
November 4, 2021 Forthcoming by Chapman and Hall/CRC
402 Pages 57 Color & 8 B/W Illustrations

USD $59.95

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Book Description

Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing.

This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are. 

Table of Contents

I Natural Language Features

1. Language and modeling

Linguistics for text analysis

A glimpse into one area: morphology

Different languages

Other ways text can vary

Summary

2. Tokenization

What is a token?

Types of tokens

Character tokens

Word tokens

Tokenizing by n-grams

Lines, sentence, and paragraph tokens

Where does tokenization break down?

Building your own tokenizer

Tokenize to characters, only keeping letters

Allow for hyphenated words

Wrapping it in a function

Tokenization for non-Latin alphabets

Tokenization benchmark

Summary

3. Stop words

Using premade stop word lists

Stop word removal in R

Creating your own stop words list

All stop word lists are context-specific

What happens when you remove stop words

Stop words in languages other than English

Summary

4. Stemming

How to stem text in R

Should you use stemming at all?

Understand a stemming algorithm

Handling punctuation when stemming

Compare some stemming options

Lemmatization and stemming

Stemming and stop words

Summary

5. Word Embeddings

Motivating embeddings for sparse, high-dimensional data

Understand word embeddings by finding them yourself

Exploring CFPB word embeddings

Use pre-trained word embeddings

Fairness and word embeddings

Using word embeddings in the real world

Summary

II Machine Learning Methods

Regression

A first regression model

Building our first regression model

Evaluation

Compare to the null model

Compare to a random forest model

Case study: removing stop words

Case study: varying n-grams

Case study: lemmatization

Case study: feature hashing

Text normalization

What evaluation metrics are appropriate?

The full game: regression

Preprocess the data

Specify the model

Tune the model

Evaluate the modeling

Summary

Classification

A first classification model

Building our first classification model

Evaluation

Compare to the null model

Compare to a lasso classification model

Tuning lasso hyperparameters

Case study: sparse encoding

Two class or multiclass?

Case study: including non-text data

Case study: data censoring

Case study: custom features

Detect credit cards

Calculate percentage censoring

Detect monetary amounts

What evaluation metrics are appropriate?

The full game: classification

Feature selection

Specify the model

Evaluate the modeling

Summary

III Deep Learning Methods

Dense neural networks

Kickstarter data

A first deep learning model

Preprocessing for deep learning

One-hot sequence embedding of text

Simple flattened dense network

Evaluation

Using bag-of-words features

Using pre-trained word embeddings

Cross-validation for deep learning models

Compare and evaluate DNN models

Limitations of deep learning

Summary

Long short-term memory (LSTM) networks

A first LSTM model

Building an LSTM

Evaluation

Compare to a recurrent neural network

Case study: bidirectional LSTM

Case study: stacking LSTM layers

Case study: padding

Case study: training a regression model

Case study: vocabulary size

The full game: LSTM

Preprocess the data

Specify the model

Summary

Convolutional neural networks

What are CNNs?

Kernel

Kernel size

A first CNN model

Case study: adding more layers

Case study: byte pair encoding

Case study: explainability with LIME

Case study: hyperparameter search

The full game: CNN

Preprocess the data

Specify the model

Summary

IV Conclusion

Text models in the real world

Appendix

A Regular expressions

A Literal characters

A Meta characters

A Full stop, the wildcard

A Character classes

A Shorthand character classes

A Quantifiers

A Anchors

A Additional resources

B Data

B Hans Christian Andersen fairy tales

B Opinions of the Supreme Court of the United States

B Consumer Financial Protection Bureau (CFPB) complaints

B Kickstarter campaign blurbs

C Baseline linear classifier

C Read in the data

C Split into test/train and create resampling folds

C Recipe for data preprocessing

C Lasso regularized classification model

C A model workflow

C Tune the workflow

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Author(s)

Biography

Emil Hvitfeldt is a clinical data analyst working in healthcare, and an adjunct professor at American University where he is teaching statistical machine learning with tidymodels. He is also an open source R developer and author of the textrecipes package.

Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker and educator, and a real-world practitioner focusing on data analysis and machine learning practice.

Reviews

"I find this book very useful, as predictive modelling with text is an important field in data science and statistics, and yet the one that has been consistently under-represented in technical literature. Given the growing volume, complexity and accessibility of unstructured data sources, as well as the rapid development of NLP algorithms, knowledge and skills in this domain is in increasing demand. In particular, there’s a demand for pragmatic guidelines that offer not just the theoretical background to the NLP issues but also explain the end-to-end modelling process and good practices supported with code examples, just like "Supervised Machine Learning for Text Analysis in R" does. Data scientists and computational linguists would be a prime audience for this kind of publication and would most likely use it as both, (coding) reference and a textbook."
~Kasia Kulma, data science consultant

"This book fills a critical gap between the plethora of text mining books (even in R) that are too basic for practical use and the more complex text mining books that are not accessible to most data scientists. In addition, this book uses statistical techniques to do text mining and text prediction and classification. Not all text mining books take this approach, and given the level of this book, it is one of its strongest features."
~Carol Haney, Quatrics

"This book would be valuable for advanced undergraduates and early PhD students in a wide range of areas that have started using text as data…The main strength of the book is its connection to the tidyverse environment in R. It's relatively easy to pick up and do powerful things."
~David Mimno, Cornell University

"The authors do a great job of presenting R programmers a variety of deep learning applications to text-based problems. Perhaps one of the best parts of this book is the section on interpretability, where the authors showcase methods to diagnose features on which these complex models rely to make their prediction. Considering how important the area of interpretability is to natural language processing research and is often skipped in applied textbooks, the authors should be commended for incorporating it in this book."
~Kanishka Misra, Purdue University