EBOOK
About
Master text-taming techniques and build effective text-processing applications with R
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.
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.
This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.
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.
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.
This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.