Text Mining in Practice with R Book [PDF] Download

Download the fantastic book titled Text Mining in Practice with R written by Ted Kwartler, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Text Mining in Practice with R", which was released on 24 July 2017. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Mathematics genre.

Summary of Text Mining in Practice with R by Ted Kwartler PDF

A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You’ll learn how to: Identify actionable social media posts to improve customer service Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.


Detail About Text Mining in Practice with R PDF

  • Author : Ted Kwartler
  • Publisher : John Wiley & Sons
  • Genre : Mathematics
  • Total Pages : 320 pages
  • ISBN : 1119282012
  • PDF File Size : 21,6 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Text Mining in Practice with R

Text Mining in Practice with R
  • Publisher : John Wiley & Sons
  • File Size : 28,9 Mb
  • Release Date : 24 July 2017
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A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by

Text Mining with R

Text Mining with R
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 24,9 Mb
  • Release Date : 12 June 2017
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Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining

Introduction to Data Science

Introduction to Data Science
  • Publisher : CRC Press
  • File Size : 48,6 Mb
  • Release Date : 20 November 2019
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical

Supervised Machine Learning for Text Analysis in R

Supervised Machine Learning for Text Analysis in R
  • Publisher : CRC Press
  • File Size : 37,8 Mb
  • Release Date : 22 October 2021
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Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine

Text Analysis with R

Text Analysis with R
  • Publisher : Springer Nature
  • File Size : 20,7 Mb
  • Release Date : 30 March 2020
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Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming

R for Data Science

R for Data Science
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 36,8 Mb
  • Release Date : 12 December 2016
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed

Text Mining and Analysis

Text Mining and Analysis
  • Publisher : SAS Institute
  • File Size : 50,5 Mb
  • Release Date : 22 November 2014
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Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources