Master text-taming techniques and build effective text-processing applications with R
About This Book
- This book will help you develop an in-depth understanding of the text mining process with lucid implementation in the R language
- After reading this book, you will be able to enhance your skills on building text-mining apps with R
- All the examples in the book use the latest version of R, making this book an update-to-date edition in the market
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
- Get an introduction to various approaches in opinion mining and their implementation in R
Text Mining (or text data mining or text analytics) is a process of extracting useful and high-quality information from text by devising patterns and trends through machine learning, statistical pattern learning, and related algorithms and methods. R provides an extensive ecosystem to mine text through its many frameworks and packages.
This book will help you develop a thorough understanding of the steps in the text mining process and gain confidence in applying the concepts to build text-data driven products.
Starting with basic information about the statistics concepts used in text mining, the book will teach you how to access, cleanse, and process text using the R language and teach you how to analyze them. It 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, the book will teach you different dimensionality reduction techniques and their implementation in R, along with topic modeling, text summarization, and extracting hidden themes from documents and collections. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. You will learn the concept of an opinion in a text document and be able to apply various techniques to extract a sentiment and opinion out of it.
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.
|Sold by:||Barnes & Noble|
|File size:||8 MB|
About the Author
Kumar Ashish is an IIM alumnus, yet an engineer at heart .He has an extensive experience on data science, machine learning and natural language processing; having worked at organizations like McAfee-Intel, an ambitious data science startup, Volt consulting, and presently associated to the software & research lab of a leading MNC. Apart from work, Ashish also participates in data science competitions at Kaggle, in his spare time.
Avinash Paul A. is a programming language enthusiast, who loves exploring open sources technologies and programmer by choice. He has about 9 years of programming experience. He has worked in various companies like Sabre , McAfee , and Mindtree, and has experience in data-driven product development. He was intrigued by data science and data mining while developing niche product in education space for a ambitious data science start up. He believes data science can solve lot of societal challenges. In his spare time, he loves to read technical books and teach underprivileged children back home.