Text Mining With R

Text Mining with R PDF
Author: Julia Silge
Publisher: "O'Reilly Media, Inc."
ISBN: 1491981628
Size: 25.83 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 194
View: 2438

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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 tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext 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 ggraph 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

Mastering Text Mining With R

Mastering Text Mining with R PDF
Author: Ashish Kumar
Publisher: Packt Publishing Ltd
ISBN: 1782174702
Size: 20.44 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 258
View: 7226

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Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

Text Mining In Practice With R

Text Mining in Practice with R PDF
Author: Ted Kwartler
Publisher: John Wiley & Sons
ISBN: 1119282012
Size: 54.55 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 320
View: 6442

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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 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.

Mastering Text Mining With R

Mastering Text Mining with R PDF
Author: Kumar Ashish
Publisher:
ISBN: 9781783551811
Size: 78.80 MB
Format: PDF, Mobi
Category :
Languages : en
Pages : 288
View: 737

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Master text-taming techniques and build effective text-processing applications with RAbout This Book* This book will help you develop an in-depth understanding of the text mining process with lucid implementation in the R language* After reading this book, you will be able to enhance your skills on building text-mining apps with R* All the examples in the book use the latest version of R, making this book an update-to-date edition in the marketWho This Book Is ForIf you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful.What You Will Learn* Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process* Access and manipulate data from different sources such as JSON and HTTP* Process text using regular expressions* Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis* Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R* Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA)* Build a baseline sentence completing application* Perform entity extraction and named entity recognition using R* Get an introduction to various approaches in opinion mining and their implementation in RIn DetailText Mining (or text data mining or text analytics) is a process of extracting useful and high-quality information from text by devising patterns and trends through machine learning, statistical pattern learning, and related algorithms and methods. R provides an extensive ecosystem to mine text through its many frameworks and packages.This book will help you develop a thorough understanding of the steps in the text mining process and gain confidence in applying the concepts to build text-data driven products.Starting with basic information about the statistics concepts used in text mining, the book will teach you how to access, cleanse, and process text using the R language and teach you how to analyze them. It will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing.Moving on, the book will teach you different dimensionality reduction techniques and their implementation in R, along with topic modeling, text summarization, and extracting hidden themes from documents and collections. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. You will learn the concept of an opinion in a text document and be able to apply various techniques to extract a sentiment and opinion out of it.By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.

People Analytics Text Mining With R

People Analytics   Text Mining with R PDF
Author: Mong Shen Ng
Publisher: Independently Published
ISBN: 9781091118539
Size: 78.35 MB
Format: PDF, ePub, Mobi
Category : Business & Economics
Languages : en
Pages : 372
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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.

Text Mining With R

Text Mining with R PDF
Author: Julia Silge. David Robinson
Publisher:
ISBN: 9781491981641
Size: 30.65 MB
Format: PDF, Docs
Category :
Languages : en
Pages :
View: 3076

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Die Abtei Von Northanger

Die Abtei von Northanger PDF
Author: Jane Austen
Publisher: mehrbuch
ISBN: 3969443032
Size: 49.61 MB
Format: PDF, Kindle
Category : Fiction
Languages : de
Pages : 300
View: 3348

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Kloster Northanger ist ein unterhaltsamer, erzähltechnisch erstaunlich moderner Roman, der ganz nebenbei die vornehme britische Gesellschaft mit ihren Sprach- und Lese-Konventionen auf den Arm nimmt. Jane Austens postum erschienenes Werk Kloster Northanger ist der erste ihrer Romane, den die Autorin zur Veröffentlichung bestimmte.

Umwitterte H Hen

Umwitterte H  hen PDF
Author: Emily Brontë
Publisher: FV Éditions
ISBN:
Size: 46.22 MB
Format: PDF, Kindle
Category : Fiction
Languages : de
Pages : 340
View: 3722

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Eine zeitlose und einzigartige Liebesgeschichte...

Text Analysis With R For Students Of Literature

Text Analysis with R for Students of Literature PDF
Author: Matthew L. Jockers
Publisher: Springer
ISBN: 3319031643
Size: 20.22 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 194
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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 Mining Wissensrohstoff Text

Text Mining  Wissensrohstoff Text PDF
Author: Gerhard Heyer
Publisher:
ISBN: 9783937137308
Size: 36.11 MB
Format: PDF, ePub
Category : Text Mining
Languages : de
Pages : 348
View: 1514

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Ein großer Teil des Weltwissens liegt in Form digitaler Texte im Internet und in Intranets. Diese digitalen Texte - die in den meisten natürlichen Sprachen vorliegen - stellen einen bedeutsamen und bisher kaum genutzten Wissensrohstoff dar. Lernen Sie in diesem ersten deutschen Lehrbuch zu diesem Thema, wie digitaler Text mit Hilfe des ”Text Mining“ für das Wissensmanagement aufbereitet, verarbeitet und genutzt werden kann. Die behandelten Themen in diesem Buch: Wissen und Text, Grundlagen der Bedeutungsanalyse, Textdatenbanken, Sprachstatistik, Clustering, Musteranalyse, Hybride Verfahren, Beispielanwendungen, Anhänge: Statistik und linguistische Grundlagen.

Data Mining With R

Data Mining with R PDF
Author: Luis Torgo
Publisher: CRC Press
ISBN: 1315399091
Size: 56.61 MB
Format: PDF, ePub, Docs
Category : Business & Economics
Languages : en
Pages : 426
View: 3312

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Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Carol

Carol PDF
Author: Patricia Highsmith
Publisher:
ISBN: 9783763240074
Size: 32.74 MB
Format: PDF, ePub
Category :
Languages : en
Pages : 405
View: 6405

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Datenanalyse Mit Python

Datenanalyse mit Python PDF
Author: Wes McKinney
Publisher: O'Reilly
ISBN: 3960102135
Size: 61.68 MB
Format: PDF, Mobi
Category : Computers
Languages : de
Pages : 542
View: 6851

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Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen. Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar. Aus dem Inhalt: Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative Computing Lernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennen Setzen Sie die Datenanalyse-Tools der pandasBibliothek ein Verwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von Daten Erstellen Sie interformative Visualisierungen mit matplotlib Wenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassen Analysieren und manipulieren Sie verschiedenste Zeitreihen-Daten Für diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Factfulness

Factfulness PDF
Author: Hans Rosling
Publisher: Ullstein Buchverlage
ISBN: 3843717451
Size: 13.62 MB
Format: PDF, ePub
Category : Science
Languages : de
Pages : 350
View: 7491

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Es wird alles immer schlimmer, eine schreckliche Nachricht jagt die andere: Die Reichen werden reicher, die Armen ärmer. Es gibt immer mehr Kriege, Gewaltverbrechen, Naturkatastrophen. Viele Menschen tragen solche beängstigenden Bilder im Kopf. Doch sie liegen damit grundfalsch. Unser Gehirn verführt uns zu einer dramatisierenden Weltsicht, die mitnichten der Realität entspricht, wie der geniale Statistiker und Wissenschaftler Hans Rosling erklärt. Wer das Buch gelesen hat, wird • ein sicheres, auf Fakten basierendes Gerüst besitzen, um die Welt so zu sehen, wie sie wirklich ist • die zehn gängigsten Arten von aufgebauschten Geschichten erkennen • bessere Entscheidungen treffen können • wahre Factfulness erreichen – jene offene, neugierige und entspannte Geisteshaltung, in der Sie nur noch Ansichten teilen und Urteile fällen, die auf soliden Fakten basieren

Im Giftstrom

Im Giftstrom PDF
Author: Arthur Conan Doyle
Publisher: BoD – Books on Demand
ISBN: 3739209372
Size: 60.82 MB
Format: PDF, ePub
Category : Fiction
Languages : de
Pages : 140
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Der ebenso streitbare wie exzentrische Professor Challanger findet Anzeichen dafür, dass die Erde auf ein Giftstrom zu treibt und das gesamte Leben auf dem Planeten in Gefahr ist. Gemeinsam mit seinen Mitstreitern Professor Summerlee, Lord John Roxton und dem Reporter Malone versucht er, das Ende der Welt abzuwenden... Ein viktorianischer Science-Fiction-Roman aus der Feder des Sherlock Holmes-Erfinder Arthur Conan Doyle.