Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2016
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781783551811/?ar |
| Zusammenfassung: | Master text-taming techniques and build effective text-processing applications with R 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 completing application Perform entity extraction and named entity recognition using R In Detail 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 m... |
| Beschreibung: | Includes index. - Online resource; title from cover (Safari, viewed January 25, 2017) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781782174707 1782174702 9781783551811 |
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| 520 | |a Master text-taming techniques and build effective text-processing applications with R 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 completing application Perform entity extraction and named entity recognition using R In Detail 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 m... | ||
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| spelling | Kumar, Ashish VerfasserIn aut Mastering text mining with R master text-taming techniques and build effective text-processing applications with R Ashish Kumar, Avinash Paul Birmingham, UK Packt Publishing 2016 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Online resource; title from cover (Safari, viewed January 25, 2017) Master text-taming techniques and build effective text-processing applications with R 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 completing application Perform entity extraction and named entity recognition using R In Detail 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 m... Text processing (Computer science) R (Computer program language) Data mining Application software Development Word processing operations Word processing Traitement de texte R (Langage de programmation) Exploration de données (Informatique) Logiciels d'application ; Développement COMPUTERS ; General Application software ; Development Paul, Avinash VerfasserIn aut 178355181X Erscheint auch als Druck-Ausgabe 178355181X |
| spellingShingle | Kumar, Ashish Paul, Avinash Mastering text mining with R master text-taming techniques and build effective text-processing applications with R Text processing (Computer science) R (Computer program language) Data mining Application software Development Word processing operations Word processing Traitement de texte R (Langage de programmation) Exploration de données (Informatique) Logiciels d'application ; Développement COMPUTERS ; General Application software ; Development |
| title | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R |
| title_auth | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R |
| title_exact_search | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R |
| title_full | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R Ashish Kumar, Avinash Paul |
| title_fullStr | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R Ashish Kumar, Avinash Paul |
| title_full_unstemmed | Mastering text mining with R master text-taming techniques and build effective text-processing applications with R Ashish Kumar, Avinash Paul |
| title_short | Mastering text mining with R |
| title_sort | mastering text mining with r master text taming techniques and build effective text processing applications with r |
| title_sub | master text-taming techniques and build effective text-processing applications with R |
| topic | Text processing (Computer science) R (Computer program language) Data mining Application software Development Word processing operations Word processing Traitement de texte R (Langage de programmation) Exploration de données (Informatique) Logiciels d'application ; Développement COMPUTERS ; General Application software ; Development |
| topic_facet | Text processing (Computer science) R (Computer program language) Data mining Application software Development Word processing operations Word processing Traitement de texte R (Langage de programmation) Exploration de données (Informatique) Logiciels d'application ; Développement COMPUTERS ; General Application software ; Development |
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