Supervised Machine Learning for Text Analysis in R

Supervised Machine Learning for Text Analysis in R

Author: EMIL. SILGE HVITFELDT (JULIA.)

Publisher: CRC Press

ISBN: 0367554194

Category:

Page: 392

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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.
Supervised Machine Learning for Text Analysis in R
Language: en
Pages: 392
Authors: EMIL. SILGE HVITFELDT (JULIA.), Julia Silge
Categories:
Type: BOOK - Published: 2021-10-22 - Publisher: CRC Press

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
Supervised Machine Learning for Text Analysis in R
Language: en
Pages: 402
Authors: Emil Hvitfeldt, Julia Silge
Categories: Computers
Type: BOOK - Published: 2021-10-22 - Publisher: CRC Press

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
Supervised Machine Learning for Text Analysis in R
Language: en
Pages: 392
Authors: Emil Hvitfeldt, Julia Silge
Categories: Computers
Type: BOOK - Published: 2021-10-22 - Publisher: CRC Press

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
Public Policy Analytics
Language: en
Pages: 228
Authors: Ken Steif
Categories: Business & Economics
Type: BOOK - Published: 2021-08-19 - Publisher: CRC Press

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and
Conducting Sentiment Analysis
Language: en
Pages:
Authors: Lei Lei, Dilin Liu
Categories: Language Arts & Disciplines
Type: BOOK - Published: 2021-08-31 - Publisher: Cambridge University Press

This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment