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008			220920b ||||| |||| 00| 0 eng d
020			_a9789352135769
082			_a519.502855133 _bSIL
100			_aSilge, Julia _98764
245			_aText mining with R: a tidy approach
260			_bO'Reilly Media _aMumbai _c2021
300			_axii, 178 p.
365			_aINR _b675.00
520			_aAll 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
650			_aData mining _9365
650			_aR (Computer program language) _91512
650			_aNatural language processing (Computer science) _97016
650			_aDiscourse analysis--Data processing _98765
942			_2ddc _cBK