Text Mining with R

Julia Silge

ISBN 10: 1491981652 / ISBN 13: 9781491981658

Verlag: O?Reilly Media, Inc, USA, 2017

Beschreibung

2017. 1st Edition. Paperback. Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem. Num Pages: 150 pages. BIC Classification: UYQL. Category: (XV) Technical / Manuals. Dimension: 250 x 150 x 15. Weight in Grams: 666. . . . . .

Bestandsnummer des Verkäufers V9781491981658

Bibliografische Details

• Titel: Text Mining with R
• Verlag: O?Reilly Media, Inc, USA
• Erscheinungsjahr: 2017
• Sprache: Englisch
• ISBN-10: 1491981652
• ISBN-13: 9781491981658
• Einband: Softcover
• Auflage: 1. Auflage
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

• Learn how to apply the tidy text format to NLP
• Use sentiment analysis to mine the emotional content of text
• Identify a document's most important terms with frequency measurements
• Explore relationships and connections between words with the ggraph and widyr packages
• Convert back and forth between R's tidy and non-tidy text formats
• Use topic modeling to classify document collections into natural groups
• Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

Über die Autorin bzw. den Autor

Julia Silge is a data scientist at Datassist where her work involves analyzing and modeling complex data sets while communicating about technical topics with diverse audiences. She has a PhD in Astrophysics, as well as abiding affections for Jane Austen and making beautiful charts. Julia worked in academia and ed tech before moving into data science and discovering R. David Robinson is a data scientist at Stack Overflow. He has a PhD in Quantitative and Computational Biology from Princeton University, where he worked with Professor John Storey on genomic analysis. He enjoys working and blogging about statistics, R programming, and text mining, including a popular analysis of Donald Trump's twitter account (performed according to the tidy data principles described in this book).