Supervised Machine Learning for Text Analysis in R

Name: Supervised Machine Learning for Text Analysis in R
Brand: Chapman & Hall/CRC
Price: 68.49 EUR
Availability: OnlineOnly

Emil Hvitfeldt Julia Silge(Autor*in)

Chapman & Hall/CRC (Verlag)

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Erschienen am 3. November 2021

402 Seiten

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Beschreibung

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.

Rezensionen / Stimmen

"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

Supervised Machine Learning for Text Analysis in R

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