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 text mining process with lucid
implementation in the R language
* Example-rich guide that lets you gain high-quality 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 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.
STYLE AND APPROACH
This book takes a hands-on, example-driven approach to the text mining
process with lucid implementation in R.
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Produktdetaljer
ISBN
9781782174707
Publisert
2016
Utgave
1. utgave
Utgiver
Vendor
Packt Publishing
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter