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Master text-taming techniques and build effective text-processing applications with R

• 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

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.

• 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

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.