Text Analysis With R

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Text Analysis With R

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Author : Matthew L. Jockers
language : en
Publisher: Springer Nature
Release Date : 2020-03-30

Text Analysis With R written by Matthew L. Jockers and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with Computers categories.

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 language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.

Text Mining With R

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Author : Julia Silge
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-12

Text Mining With R written by Julia Silge and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.

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 NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.

Quantitative Text Analysis Using R

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Author : Julian Bernauer
language : en
Publisher: SAGE Publications Limited
Release Date : 2025-03-14

Quantitative Text Analysis Using R written by Julian Bernauer and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Social Science categories.

Grounded in examples from across the social sciences, this book walks you through the process of doing quantitative text analysis step by step. Clear and accessible, it empowers you to progress from beginner level to understanding and using computational social science concepts with ease. Covering key steps in the research process like ethics, data collection, and model choice, it helps you develop important research skills – and equips you with the programming tools you need to handle text data without error. The textbook offers R software guidance at an easy-to-follow pace, the book presents the coding skills you need to collect and prepare data, providing a strong foundation as you move into data analysis. It will: · Help you develop key data skills like cleaning, managing, classifying and visualizing data · Encourage your ability to be critical and reflective when dealing with data · Offer clear guidance on using messy, real-world data and big data from sources like Wikipedia Supported by practical online resources including extensive coding examples and software guidance, this book will give you confidence in applying your programming skills and enable you to take control of handling textual data in your own research.

Text Analysis With R For Students Of Literature

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Author : Matthew Jockers
language : en
Publisher: Springer
Release Date : 2014-06-14

Text Analysis With R For Students Of Literature written by Matthew Jockers and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-14 with Computers categories.

Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.

Text Analysis With R

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Author : Matthew Lee Jockers
language : en
Publisher:
Release Date : 2020

Text Analysis With R written by Matthew Lee Jockers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computational linguistics categories.

This practical introduction explores core R procedures and processes and offers a thorough understanding of the possibilities of computational text analysis at both micro and macro scales. Each chapter concludes with a set of practice exercises.

Text Mining In Practice With R

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Author : Ted Kwartler
language : en
Publisher: John Wiley & Sons
Release Date : 2017-07-24

Text Mining In Practice With R written by Ted Kwartler and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-24 with Mathematics categories.

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.

People Analytics Text Mining With R

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Author : Mong Shen Ng
language : en
Publisher: Independently Published
Release Date : 2019-03-21

People Analytics Text Mining With R written by Mong Shen Ng and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-21 with Business & Economics categories.

You don't need to buy expensive statistical software like SPSS. This book teaches you R (R can be downloaded for free), People Analytics, Social Media Analytics, Text Mining and Sentiment Analysis. It is written for people with absolutely NO knowledge of R programming, with step-by-step print-screen instructions. The sample R codes are kept simple & short so that you are not overwhelmed with too much unnecessary information, and focuses on teaching you the R codes relevant to people analytics, so that you'll be up-and-running in no time. If you are new to R programming, this is the book for you. As R is developed specially for statistical analysis, you can run complicated statistical number crunching (Correlation, Multiple & Logistic Regression, etc.) by simply entering a few commands. This book covers the full People Analytics scope (Benefits, Compensation, Culture, Diversity & Inclusion, Engagement, Leadership, Learning & Development, Personality Traits, Performance Management, Recruitment, Sales Incentives) with numerous real-world examples, and shows how R programming can help you: 1) Run Social Media Analytics, Text mining & Sentiment Analysis with R. 2) Predict employees' flight-risk using R's Correlation & Logistic Regression function. 3) Identify the personality traits of top performing Customer Service staff and Sales staff using R's correlation function. 4) Predict impact of Employee Engagement on Customer Satisfaction, Revenue and Shareholder Returns, etc. using R's Correlation & Multiple Regression function. 5) Predict impact of Learning & Development on Sales, using R's Multiple Regression function. 6) Predict Diversity & Inclusion's impact on Revenue and EBIT using R's Multiple Regression function.

Beginner S Guide To R Programming

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Author : Agasti Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-02-20

Beginner S Guide To R Programming written by Agasti Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.

Discover the world of data analysis with "Beginner's Guide to R Programming." This comprehensive resource is crafted to help individuals learn the R programming language and explore its diverse applications. Whether you're a complete beginner or an experienced analyst, our book offers a structured learning path that starts with the basics and progresses to advanced topics like statistical analysis, data visualization, and machine learning. Each chapter includes practical examples, exercises, and real-world case studies, encouraging hands-on experimentation with R code. You'll delve into data types, functions, data manipulation, statistical analysis, data visualization, and more, building a solid foundation in R programming and data analysis. Complex concepts are explained in clear, easy-to-understand language, with visual aids, code snippets, and step-by-step tutorials to help you grasp key ideas effectively. The book emphasizes practical applications of R in real-world scenarios, showcasing how you can use R to solve problems, analyze data, make informed decisions, and communicate insights. With access to supplementary resources, including downloadable datasets, code samples, and additional exercises, you'll further enhance your learning experience and practice your skills.

Modeling Techniques In Predictive Analytics With Python And R

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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-09-29

Modeling Techniques In Predictive Analytics With Python And R written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-29 with Business & Economics categories.

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Foundations Of Data Science And Ai Using R

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Author : R.Ganesh moorthi
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-12-08

Foundations Of Data Science And Ai Using R written by R.Ganesh moorthi and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-08 with Computers categories.

R.Ganesh moorthi, Assistant Professor, Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.