Tools

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008 170712s2017 caua b 001 0 eng d;

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016 7 a| 0183314102| Uk;

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020 a| 9781491981658q| (paperback);

020 a| 1491981652q| (paperback);

029 1 a| UKMGBb| 018331410;

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082 04 a| 006.352| 231| https://id.oclc.org/worldcat/ddc/E49gC4CchdgPKmmFw3hwmFJ4Vf;

100 1 a| Silge, Julia,e| author.=| ^A1483885;

245 10 a| Text mining with R :b| a tidy approach /c| Julia Silge and David Robinson.;

250 a| First edition.;

264 1 a| Sebastopol, CA :b| O'Reilly,c| 2017.;

300 a| xii, 178 pages :b| illustrations ;c| 24 cm;

336 a| textb| txt2| rdacontent;

337 a| unmediatedb| n2| rdamedia;

338 a| volumeb| nc2| rdacarrier;

504 a| Includes bibliographical references (pages 173-174) and index.;

520 a| "Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media"--c| Provided by Publisher.;

505 0 a| Chapter 1 : The Tidy Text Format -- Chapter 2 : Sentiment Analysis with Tidy Data -- Chapter 3 : Analyzing Word and Document Frequency: tf-idf -- Chapter 4 : Relationships Between Words: N-grams and Correlations -- Chapter 5 : Converting to and from Nontidy Formats -- Chapter 6 : Topic Modeling -- Chapter 7 : Case Study: Comparing Twitter Archives -- Chapter 8 : Case Study: Mining NASA Metadata -- Chapter 9 : Case Study: Analyzing Usenet Text.;

650 0 a| R (Computer program language)=| ^A523882;

650 0 a| Data mining.=| ^A408868;

650 0 a| Discourse analysisx| Data processing.=| ^A138579;

650 0 a| Natural language processing (Computer science)=| ^A235570;

650 2 a| Data Mining.0| (DNLM)D057225=| ^A1023265;

650 2 a| Natural Language Processing.0| (DNLM)D009323=| ^A1462496;

650 7 a| Natural language processing (Computer science)2| fast0| (OCoLC)fst01034365;

650 7 a| Discourse analysisx| Data processing.2| fast0| (OCoLC)fst00894934;

650 7 a| Data mining.2| fast0| (OCoLC)fst00887946;

650 7 a| R (Computer program language)2| fast0| (OCoLC)fst01086207;

758 i| has work:a| Text mining with R (Text)1| https://id.oclc.org/worldcat/entity/E39PCGfyQHPprr3RC4MYKbJPw34| https://id.oclc.org/worldcat/ontology/hasWork;

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Text mining with R : a tidy approach / Julia Silge and David Robinson.

Author/creator	Silge, Julia author.
Other author	Robinson, David (Data scientist), author.
Format	Book
Edition	First edition.
Publication	Sebastopol, CA : O'Reilly, 2017.
Copyright Date	©2017
Description	xii, 178 pages : illustrations ; 24 cm
Subjects	R (Computer program language) Data mining. Discourse analysis--Data processing. Natural language processing (Computer science) Data Mining. Natural Language Processing.

Contents	Chapter 1 : The Tidy Text Format -- Chapter 2 : Sentiment Analysis with Tidy Data -- Chapter 3 : Analyzing Word and Document Frequency: tf-idf -- Chapter 4 : Relationships Between Words: N-grams and Correlations -- Chapter 5 : Converting to and from Nontidy Formats -- Chapter 6 : Topic Modeling -- Chapter 7 : Case Study: Comparing Twitter Archives -- Chapter 8 : Case Study: Mining NASA Metadata -- Chapter 9 : Case Study: Analyzing Usenet Text.
Abstract	"Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media"-- Provided by Publisher.
Bibliography note	Includes bibliographical references (pages 173-174) and index.
Issued in other form	Online version: Silge, Julia. Text mining with R. First edition. Bejing ; Boston : O'Reilly, 2017 9781491981627
LCCN	2017471546
ISBN	9781491981658 (paperback)
ISBN	1491981652 (paperback)

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Text mining with R : a tidy approach / Julia Silge and David Robinson.

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