Mastering Text Mining with R

Master text-taming techniques and build effective text-processing applications with R

Mastering Text Mining with R

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Ashish Kumar, Avinash Paul

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Master text-taming techniques and build effective text-processing applications with R
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Book Details

ISBN 139781783551811
Paperback258 pages

Book Description

Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality 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.

Table of Contents

Chapter 1: Statistical Linguistics with R
Probability theory and basic statistics
Language models
Quantitative methods in linguistics
R packages for text mining
Summary
Chapter 2: Processing Text
Accessing text from diverse sources
Processing text using regular expressions
Normalizing texts
Lexical diversity
Language detection
Summary
Chapter 3: Categorizing and Tagging Text
Parts of speech tagging
Hidden Markov Models for POS tagging
Collocation and contingency tables
Feature extraction
Summary
Chapter 4: Dimensionality Reduction
The curse of dimensionality
Dimensionality reduction
Correspondence analysis
Summary
Chapter 5: Text Summarization and Clustering
Topic modeling
Latent semantic analysis
Text clustering
Document clustering
Sentence completion
Summary
Chapter 6: Text Classification
Text classification
Document representation
Kernel methods
Bias–variance trade-off and learning curve
Learning curve
Dealing with reducible error components
Summary
Chapter 7: Entity Recognition
Entity extraction
Sentence boundary detection
Named entity recognition
Summary

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

Authors

Table of Contents

Chapter 1: Statistical Linguistics with R
Probability theory and basic statistics
Language models
Quantitative methods in linguistics
R packages for text mining
Summary
Chapter 2: Processing Text
Accessing text from diverse sources
Processing text using regular expressions
Normalizing texts
Lexical diversity
Language detection
Summary
Chapter 3: Categorizing and Tagging Text
Parts of speech tagging
Hidden Markov Models for POS tagging
Collocation and contingency tables
Feature extraction
Summary
Chapter 4: Dimensionality Reduction
The curse of dimensionality
Dimensionality reduction
Correspondence analysis
Summary
Chapter 5: Text Summarization and Clustering
Topic modeling
Latent semantic analysis
Text clustering
Document clustering
Sentence completion
Summary
Chapter 6: Text Classification
Text classification
Document representation
Kernel methods
Bias–variance trade-off and learning curve
Learning curve
Dealing with reducible error components
Summary
Chapter 7: Entity Recognition
Entity extraction
Sentence boundary detection
Named entity recognition
Summary

Book Details

ISBN 139781783551811
Paperback258 pages
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