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Synopsis
About This Book
- 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
Who This Book Is 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
In Detail
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. 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.
"synopsis" may belong to another edition of this title.
About the Author
Ashish Kumar
Ashish Kumar is an IIM alumnus and an engineer at heart. He has extensive experience in data science, machine learning, and natural language processing having worked at organizations, such as McAfee-Intel, an ambitious data science startup Volt consulting), and presently associated to the software and research lab of a leading MNC. Apart from work, Ashish also participates in data science competitions at Kaggle in his spare time.
"About this title" may belong to another edition of this title.
- PublisherPackt Publishing
- Publication date2016
- ISBN 10 178355181X
- ISBN 13 9781783551811
- BindingPaperback
- LanguageEnglish
- Number of pages258
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - About This BookDevelop all the relevant skills for building text-mining apps with R with this easy-to-follow guideGain 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 Who This Book Is 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 LearnGet 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 In Detail 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. 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. Seller Inventory # 9781783551811
Quantity: 1 available