Mastering Text Mining with R: Extract and recognize your text data

KUMAR ASHISH

R$245.99

2.4 (11 Ratings)

Paperback Dec 2016 258 pages 1st Edition

KUMAR ASHISH

R$245.99

2.4 (11 Ratings)

Paperback Dec 2016 258 pages 1st Edition

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Mastering Text Mining with R

Chapter 2. Processing Text

A significant part of the time spent on any modeling or analysis activity goes into accessing, preprocessing, and cleaning the data. We should have the capability to access data from diverse sources, load them in our statistical analysis environment and process them in a manner conducive for advanced analysis.

In this chapter, we will learn to access data from a wide variety of sources and load it into our R environment. We will also learn to perform some standard text processing.

By the time you finish the chapter, you should be equipped with enough knowledge to retrieve data from most of the data sources and process it into custom corpus for further analysis:

Accessing texts from diverse sources
Processing texts using regular expressions
Normalizing texts
Lexical diversity
Language detection

Lexical diversity

Consider a speaker, who uses the term allow multiple times throughout the speech, compared to an another speaker who uses terms allow, concur, acquiesce, accede, and avow for the same word. The latter speech has more lexical diversity than the former. Lexical diversity is widely believed to be an important parameter to rate a document in terms of textual richness and effectiveness.

Lexical diversity, in simple terms, is a measurement of the breadth and variety of vocabulary used in a document. The different measures of lexical diversity are TTR, MSTTR, MATTR, C, R, CTTR, U, S, K, Maas, HD-D, MTLD, and MTLD-MA.

koRpus package in R provides functions to estimate the lexical diversity or complexity.

If N is the total number of tokens and V is the number of types:

Measure	Description	Wrapper Function (koRpus package in R)
TTR	Type-Token Ratio	TTR
MSTTR	Mean segment type token ratio	MSTTR
C	logTTR	C.ld
R	Root TTR	R.ld
CTTR	Corrected TTR	CTTR
U	Uber Index	U...

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Key benefits

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

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.

Who is this book 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

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Amazon verified reviews

Akhilesh Jan 13, 2017

Nice explanation of complex concepts in simple language , with great flow . Its a good read for beginners and intermediate readers in text mining.

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ajitB Feb 15, 2018

Great compendium of state-of-the-art algos for text mining!

Dr. S. B. Bhattacharyya Jul 18, 2018

Far too many typographical errors. Examples just satisfactory.

Raman Kumar Dec 02, 2018

Good book, although if you are a beginner in NLP, dont go for it, it wont make much sense. If you are good in statiatics atleast basics this books will he helpful for some of the approaches.

Chris May 26, 2017

Barely found this book is useful.

Mastering Text Mining with R: Extract and recognize your text data

What do you get with Print?

Mastering Text Mining with R

Chapter 2. Processing Text

Accessing text from diverse sources

File system

Processing text using regular expressions

Normalizing texts

Lexical diversity

Language detection

Accessing text from diverse sources

File system

Page 1 of 7

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