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Mastering Text Mining with R
Mastering Text Mining with R

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

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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

Accessing text from diverse sources

Reading data from diverse sources for analysis, and exporting the results to another system for reporting purposes can be a daunting task that can sometimes take even more time than the real analysis. There are various sources from which we can gather text; some of them are HTML pages, social media, RSS feeds, JSON or XML, enterprise environments, and so on. The source has a very important role to play in the quality of textual data and the way we access the source. For instance, in the case of an enterprise environment, the common sources of text or data can be database and log files. In a web ecosystem, web pages are the source of data. When we consider web service applications, the sources can be JSON or XML over HTTP or HTTPS. We will look into various data sources and ways in which we can collect data from them.

File system

Reading from a file system is a very basic capability that any programming language should provide. We may have collections of...

Processing text using regular expressions

The web consists predominantly of unstructured text. One of the main tasks in web scraping is to collect the relevant information from heaps of textual data. Within the unstructured text we are often interested in specific information, especially when we want to analyze the data using quantitative methods. Specific information can include numbers such as phone numbers, zip codes, latitude, longitude, or addresses.

First, we gather the unstructured text, next we determine the recurring patterns behind the information we are looking for, and then we apply these patterns to the unstructured text to extract the information. When we are web scraping, we have to identify and extract those parts of the document that contain the relevant information. Ideally, we can do so using xpath althrough, sometimes the crucial information is hidden within values. Sometimes relevant information might be scattered across an HTML document. We need to write regular expressions...

Normalizing texts

Normalization in text basically refers to standardization or canonicalization of tokens, which we derived from documents in the previous step. The simplest scenario possible could be the case where query tokens are an exact match to the list of tokens in document, however there can be cases when that is not true. The intent of normalization is to have the query and index terms in the same form. For instance, if you query U.K., you might also be expecting U.K.

Token normalization can be performed either by implicitly creating equivalence classes or by maintaining the relations between unnormalized tokens. There might be cases where we find superficial differences in character sequences of tokens, in such cases query and index term matching becomes difficult. Consider the words anti-disciplinary and anti-disciplinary. If both these words get mapped into one term named after one of the members of the set for example anti-disciplinary, text retrieval would become so efficient...

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...

Language detection

TextCat is a text classification utility. The primary usage of TextCat is language identification. textcat package in R provides wrapper function for n-gram based text categorization and the language detection. It can detect up to 75 languages:

Library(textcat)>my.profiles <- TC_byte_profiles[names(TC_byte_profiles)]
>my.profiles

A textcat profile db of length 75.

> my.text <- c("This book is in English language",
 "Das ist ein deutscher Satz.",
 "Il s'agit d'une phrase française.",
 "Esta es una frase en espa~nol.")
 textcat(my.text, p = my.profiles)
> textcat(my.text, p = my.profiles)

[1] "english" "german"  "french"  "spanish"

Accessing text from diverse sources


Reading data from diverse sources for analysis, and exporting the results to another system for reporting purposes can be a daunting task that can sometimes take even more time than the real analysis. There are various sources from which we can gather text; some of them are HTML pages, social media, RSS feeds, JSON or XML, enterprise environments, and so on. The source has a very important role to play in the quality of textual data and the way we access the source. For instance, in the case of an enterprise environment, the common sources of text or data can be database and log files. In a web ecosystem, web pages are the source of data. When we consider web service applications, the sources can be JSON or XML over HTTP or HTTPS. We will look into various data sources and ways in which we can collect data from them.

File system

Reading from a file system is a very basic capability that any programming language should provide. We may have collections of...

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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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Publication date : Dec 28, 2016
Length: 258 pages
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Language : English
ISBN-13 : 9781783551811
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ISBN-13 : 9781783551811
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Table of Contents

8 Chapters
1. Statistical Linguistics with R Chevron down icon Chevron up icon
2. Processing Text Chevron down icon Chevron up icon
3. Categorizing and Tagging Text Chevron down icon Chevron up icon
4. Dimensionality Reduction Chevron down icon Chevron up icon
5. Text Summarization and Clustering Chevron down icon Chevron up icon
6. Text Classification Chevron down icon Chevron up icon
7. Entity Recognition Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.4
(11 Ratings)
5 star 9.1%
4 star 9.1%
3 star 18.2%
2 star 36.4%
1 star 27.3%
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Akhilesh Jan 13, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Nice explanation of complex concepts in simple language , with great flow . Its a good read for beginners and intermediate readers in text mining.
Amazon Verified review Amazon
ajitB Feb 15, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Great compendium of state-of-the-art algos for text mining!
Amazon Verified review Amazon
Dr. S. B. Bhattacharyya Jul 18, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Far too many typographical errors. Examples just satisfactory.
Amazon Verified review Amazon
Raman Kumar Dec 02, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
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.
Amazon Verified review Amazon
Chris May 26, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Barely found this book is useful.
Amazon Verified review Amazon
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