Book Image

Mastering Text Mining with R

By : KUMAR ASHISH
Book Image

Mastering Text Mining with R

By: KUMAR ASHISH

Overview of this book

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 (15 chapters)

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