Text mining with R: a tidy approach
Material type:
TextPublication details: Mumbai O'Reilly Media 2021 Description: xii, 178 pISBN: 9789352135769Subject(s): Data mining | R (Computer program language) | Natural language processing (Computer science) | Discourse analysis--Data processingDDC classification: 519.502855133 Summary: All Indian Reprints of O'Reilly are printed in Grayscale.
"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 tidy text, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like graph and dplyr. You’ll learn how tidy text 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 graph 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
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Book
|
Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 519.502855133 SIL (Browse shelf(Opens below)) | 1 | Available | 003146 |
All Indian Reprints of O'Reilly are printed in Grayscale.
"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 tidy text, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like graph and dplyr. You’ll learn how tidy text 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 graph 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
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