Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem. You’ll discover how tidy data principles can make text mining easier, more effective, and consistent by employing tools already in wide use. Text Mining with R shows you how to manipulate, summarize, and visualize the characteristics of text, sentiment analysis, tf-idf, and topic modeling. Along with tidy data methods, you’ll also examine several beginning-to-end tidy text analyses on data sources from Twitter to NASA datasets. These analyses bring together multiple text mining approaches covered in the book. Get real-world examples for implementing text mining using tidy R package Understand natural language processing concepts like sentiment analysis, tf-idf, and topic modeling Learn how to analyze unstructured, text-heavy data using R language and ecosystem
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.
This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to ...
Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the ...
Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and ...
In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of ...
Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.
This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts.
Discourse processes, 25(2):259–284, 1998. 11. C. Fonseca and P. Fleming. ... Knowledge processing on an extended wordnet. In WordNet: An Electronic Lexical ... Foundations of Statistical Natural Language Processing. MIT Press, 1999. 24.
This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas.
Providing an extensive update to the best-selling first edition, this new edition is divided into two parts.