Book description
Master texttaming techniques and build effective textprocessing applications with R
About This Book
Develop all the relevant skills for building textmining apps with R with this easytofollow guide
Gain indepth understanding of the text mining process with lucid implementation in the R language
Examplerich guide that lets you gain highquality information from text data
Who This Book Is For
If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful.
What You Will Learn
Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process
Access and manipulate data from different sources such as JSON and HTTP
Process text using regular expressions
Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis
Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R
Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA)
Build a baseline sentence completing application
Perform entity extraction and named entity recognition using R
In Detail
Text Mining (or text data mining or text analytics) is the process of extracting useful and highquality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.
Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework.
By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
Style and approach
This book takes a handson, exampledriven approach to the text mining process with lucid implementation in R.
Publisher resources
Table of contents

Mastering Text Mining with R
 Table of Contents
 Mastering Text Mining with R
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Customer Feedback
 Preface

1. Statistical Linguistics with R

Probability theory and basic statistics
 Probability space and event
 Theorem of compound probabilities
 Conditional probability
 Bayes' formula for conditional probability
 Independent events
 Random variables
 Discrete random variables
 Probability frequency function
 Probability distributions using R
 Cumulative distribution function
 Joint distribution
 Binomial distribution
 Poisson distribution
 Counting occurrences
 Zipf's law
 Heaps' law
 Lexical richness
 Language models
 Quantitative methods in linguistics
 R packages for text mining
 Summary

Probability theory and basic statistics
 2. Processing Text
 3. Categorizing and Tagging Text
 4. Dimensionality Reduction
 5. Text Summarization and Clustering

6. Text Classification
 Text classification
 Document representation
 Kernel methods
 Bias–variance tradeoff and learning curve
 Learning curve
 Dealing with reducible error components
 Summary
 7. Entity Recognition
 Index
Product information
 Title: Mastering Text Mining with R
 Author(s):
 Release date: December 2016
 Publisher(s): Packt Publishing
 ISBN: 9781783551811
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