Text Mining With R

Text Mining with R PDF
Author: Julia Silge
Publisher: "O'Reilly Media, Inc."
ISBN: 1491981628
Size: 35.65 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 194
View: 6641

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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: 17.71 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 258
View: 352

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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 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: 20.26 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 194
View: 1615

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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.

People Analytics Text Mining With R

People Analytics   Text Mining with R PDF
Author: Mong Shen Ng
Publisher: Independently Published
ISBN: 9781091118539
Size: 74.76 MB
Format: PDF, Kindle
Category : Business & Economics
Languages : en
Pages : 372
View: 3958

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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 In Practice With R

Text Mining in Practice with R PDF
Author: Ted Kwartler
Publisher: John Wiley & Sons
ISBN: 1119282098
Size: 12.13 MB
Format: PDF, Mobi
Category : Mathematics
Languages : en
Pages : 320
View: 5145

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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.

Text Mining With R

Text Mining with R PDF
Author: Julia Silge. David Robinson
Publisher:
ISBN: 9781491981641
Size: 23.62 MB
Format: PDF, ePub, Mobi
Category :
Languages : en
Pages :
View: 5946

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Die Abtei Von Northanger

Die Abtei von Northanger PDF
Author: Jane Austen
Publisher: Lindhardt og Ringhof
ISBN: 8726482851
Size: 68.54 MB
Format: PDF, ePub, Docs
Category : Fiction
Languages : de
Pages : 226
View: 2691

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Jane Austens wunderbare Satire auf die im 19. Jahrhundert so beliebten Schauerromane: Die siebzehnjährige Cathrine Morland reist in den Kurort Bath, wo sie sich schon bald in einen jungen Geistlichen, Henry Tilney, verliebt. Als sie sich mit dessen Schwester anfreundet und auf den Familiensitz in die Abtei von Northanger eingeladen wird, scheint einer Hochzeit nichts mehr im Wege zu stehen – bis eine Intrige ihnen fast einen Strich durch die Rechnung macht... Jane Austen (1775-1817) war eine britische Schriftstellerin, die vorrangig für ihre Romane weltbekannt wurde. Zwei der Romane, "Emma" und "Stolz und Vorurteil", zählen heute zu den Klassikern der Weltliteratur. Austen beschäftigte sich in ihren Texten oftmals mit den gesellschaftlichen Zwängen junger Frauen des niederen Adels.

Text Mining In Den Sozialwissenschaften

Text Mining in den Sozialwissenschaften PDF
Author: Matthias Lemke
Publisher: Springer-Verlag
ISBN: 3658072245
Size: 15.49 MB
Format: PDF
Category : Social Science
Languages : de
Pages : 423
View: 7666

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Die Analyse von Sprache ermöglicht Rückschlüsse auf Gesellschaft und Politik. Im Zeitalter digitaler Massenmedien liegt Sprache als maschinenlesbarer Text in einer Menge vor, die ohne Hilfsmittel nicht mehr angemessen zu bewältigen ist. Die maschinelle Auswertung von Textdaten kann in den Sozialwissenschaften, die Text bislang in der Regel qualitativ und weniger quantitativ, also sprachstatistisch, analysieren, wertvolle neue Erkenntnisse liefern. Vor diesem Hintergrund führt der Band in die Verwendung von Text Mining in den Sozialwissenschaften ein. Anhand exemplarischer Analysen eines Korpus von 3,5 Millionen Zeitungsartikeln zeigt er für konkrete Forschungsfragen, wie Text Mining angewandt werden kann.

Mastering Text Mining With R

Mastering Text Mining with R PDF
Author: Kumar Ashish
Publisher:
ISBN: 9781783551811
Size: 21.94 MB
Format: PDF, Kindle
Category :
Languages : en
Pages : 288
View: 1418

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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.

Text Mining Wissensrohstoff Text

Text Mining  Wissensrohstoff Text PDF
Author: Gerhard Heyer
Publisher:
ISBN: 9783937137308
Size: 57.84 MB
Format: PDF, Kindle
Category : Text Mining
Languages : de
Pages : 348
View: 6340

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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.

Der Unsichtbare

Der Unsichtbare PDF
Author: Herbert George Wells
Publisher: Null Papier Verlag
ISBN: 395418916X
Size: 73.62 MB
Format: PDF, Mobi
Category : Fiction
Languages : de
Pages : 204
View: 3314

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Die Geschichte beginnt an einem kalten, regnerischen Wintertag. Ein seltsamer Mann kehrt in das Gasthaus »Zum Fuhrmann« ein. Er ist vollkommen vermummt, sein Gesicht bandagiert, die Augen hinter dunklen Gläsern versteckt. Das anfängliche Misstrauen der Dorfbewohner gegenüber dem Fremden, der offensichtlich länger verweilen will, wandelt sich in Furcht und Abscheu, als in der Umgebung seltsame Einbrüche zu vermelden sind, die sich niemand erklären kann. Mit seiner stimmungsvollen Schauergeschichte lädt uns Wells zum Fantasieren ein: Was würden wir machen, wenn wir unsichtbar sein könnten? Neben »Krieg der Welten« und »Die Zeitmaschine« gehört »Der Unsichtbare« sicherlich zu den bekanntesten Werken von H.G. Wells. Die Geschichte eines genialen aber psychisch labilen Wissenschaftlers, der einen Weg findet, sich unsichtbar zu machen, hat schon mehrmals den Weg auf die Leinwand gefunden. Die Figur eines unsichtbaren, den Blicken verborgenen Straftäters, ist schon längst in den Kanon der bekanntesten Horrorfiguren eingegangen. Er hatte die behandschuhten Hände auf dem Rücken gefaltet und war anscheinend in Gedanken versunken. Sie bemerkte, daß der Schnee auf seinen Kleidern zu Wasser wurde und auf ihren Teppich herabtropfte. »Kann ich Ihnen Hut und Rock abnehmen, mein Herr, und sie in der Küche trocknen?«, fragte sie. »Nein«, antwortete er, ohne sich umzuwenden. Sie war nicht sicher, ob er sie verstanden hätte, und wollte schon ihre Frage wiederholen. Da wandte er den Kopf und sah sie über die Schulter hinweg an. »Ich ziehe es vor, sie anzubehalten«, erklärte er mit Nachdruck, und sie konnte bemerken, daß er eine große, blaue Brille trug und ein buschiger Backenbart seine Wangen vollkommen bedeckte. »Gut, mein Herr«, sagte sie, »wie’s gefällig ist. Das Zimmer wird gleich warm werden.« Null Papier Verlag www.null-papier.de

Emma

Emma PDF
Author: Jane Austen
Publisher:
ISBN: 9783150204047
Size: 56.74 MB
Format: PDF, Kindle
Category :
Languages : en
Pages : 600
View: 6626

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R Mining Spatial Text Web And Social Media Data

R  Mining spatial  text  web  and social media data PDF
Author: Bater Makhabel
Publisher: Packt Publishing Ltd
ISBN: 178829081X
Size: 65.63 MB
Format: PDF
Category : Computers
Languages : en
Pages : 651
View: 5603

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Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.

R Data Analysis And Visualization

R  Data Analysis and Visualization PDF
Author: Tony Fischetti
Publisher: Packt Publishing Ltd
ISBN: 1786460483
Size: 76.21 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 1783
View: 6014

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Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.

20 000 Meilen Unter Dem Meer

20 000 Meilen unter dem Meer PDF
Author: Jules Verne
Publisher: BoD – Books on Demand
ISBN: 3958701477
Size: 59.89 MB
Format: PDF, Kindle
Category : Fiction
Languages : de
Pages : 280
View: 4413

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Bei der Verfolgung eines vermeintlichen Seeungeheuers, das für zahlreiche rätselhafte Schiffsunglücke verantwortlich sein soll, landen der Meereskundler Arronax, sein Diener Conseil und der Harpunier Ned Land an Bord des mysteriösen Unterseebootes \"Nautilus“. Hier treffen sie auf den seltsamen Kapitän Nemo, der etwas zu verbergen scheint. Die Abenteurer verbringen eine Zeit auf Nemos Schiff und erleben eine faszinierende Unterwasserwelt, die Kämpfe mit Haien und Riesenkraken nicht ausschließt. Doch schon sehr bald wird klar, dass es kein Entkommen von der \"Nautilus\" gibt. Jules Verne (1828-1905) schuf in einer Epoche beschleunigten technischen Fortschritts für die damalige Zeit einzigartige Abenteuer- und Science Fiction-Romane, die ihn unsterblich machten.