Text mining with R : a tidy approach
Material type:
TextPublication details: Mumbai : Shroff Publishers, 2017Edition: First editionDescription: xii, 178 pages : illustrationsISBN: 9781491981627; 9789352135769Subject(s): R (Computer program language) | Data miningDDC classification: 006.35 Summary: 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
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
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Main Library Stacks | REF | 006.35 SIL (Browse shelf(Opens below)) | Available | 015588 |
Originally published in Sebastopol, CA. by O'Reilly Media,
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
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