Text Mining with R | A Tidy Approach

Julia Silge (u. a.)

ISBN 10: 1491981652 / ISBN 13: 9781491981658

Published by O'Reilly Media, 2017

About this Item

Text Mining with R | A Tidy Approach | Julia Silge (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2017 | O'Reilly Media | EAN 9781491981658 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 121105489

Bibliographic Details

• Title: Text Mining with R | A Tidy Approach
• Publisher: O'Reilly Media
• Publication Date: 2017
• Language: English
• ISBN 10: 1491981652
• ISBN 13: 9781491981658
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 177H x 15D millimeters 231 millimeters width by 177 millimeters height by 15 millimeters depth
• Weight: 348 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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

About the Authors

Julia Silge is a data scientist at Stack Overflow; her work involves analyzing complex datasets and communicating about technical topics with diverse audiences. She has a PhD in astrophysics and loves Jane Austen and making beautiful charts. Julia worked in academia and ed tech before moving into data science and discovering the statistical programming language R.

David Robinson is a data scientist at Stack Overflow with a PhD in Quantitative and Computational Biology from Princeton University. He enjoys developing open source R packages, including broom, gganimate, fuzzyjoin and widyr, as well as blogging about statistics, R, and text mining on his blog, Variance Explained.