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Text mining with R: a tidy approach (Record no. 3390)

MARC details
000 -LEADER
fixed length control field	02068nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20220920145012.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	220920b \|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9789352135769
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.502855133
Item number	SIL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Silge, Julia
245 ## - TITLE STATEMENT
Title	Text mining with R: a tidy approach
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	O'Reilly Media
Place of publication, distribution, etc.	Mumbai
Date of publication, distribution, etc.	2021
300 ## - PHYSICAL DESCRIPTION
Extent	xii, 178 p.
365 ## - TRADE PRICE
Price type code	INR
Price amount	675.00
520 ## - SUMMARY, ETC.
Summary, etc.	All Indian Reprints of O'Reilly are printed in Grayscale.<br/><br/>"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.<br/><br/>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.<br/><br/>Learn how to apply the tidy text format to NLP<br/>Use sentiment analysis to mine the emotional content of text<br/>Identify a document’s most important terms with frequency measurements<br/>Explore relationships and connections between words with the graph and widyr packages<br/>Convert back and forth between R’s tidy and non-tidy text formats<br/>Use topic modeling to classify document collections into natural groups<br/>Examine case studies that compare Twitter archives, dig into NASA metadata and analyze thousands of Usenet messages
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Data mining
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	R (Computer program language)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Natural language processing (Computer science)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Discourse analysis--Data processing
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Book

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Bill No	Bill Date	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Total Checkouts	Full call number	Accession Number	Date last seen	Date checked out	Copy number	Cost, replacement price	Price effective from	Koha item type
		Dewey Decimal Classification			IT & Decisions Sciences	TB1415	08-09-2022	Indian Institute of Management LRC	Indian Institute of Management LRC	General Stacks	09/20/2022	Technical Bureau India Pvt. Ltd.	472.50	1	519.502855133 SIL	003146	09/19/2023	07/27/2023	1	675.00	09/20/2022	Book