» Text Analysis with R for Students of Literature Matthew L. Jockers Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. 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. View the Book Flyer [pdf 1.4MB] Introduction to the RStudio Programming Environment [Video]. “This is a well written book on the topic of Text Analysis. “This book is an essential resource for anyone who wants to study literature using computational methods.” “I can’t think of a more qualified person to guide readers through powerful R techniques for text analysis. “The open source programming language R has become one of the most central statistical and analytical tool in many sciences. Thanks to all of those below who provided comments and feedback.
Examples of common formulas - Windows SharePoint Services - Microsoft Office Online Important notice for users of Office 2003 To continue receiving security updates for Office, make sure you're running Office 2003 Service Pack 3 (SP3). The support for Office 2003 ends April 8, 2014. If you’re running Office 2003 after support ends, to receive all important security updates for Office, you need to upgrade to a later version such as Office 365 or Office 2013. For more information, see Support is ending for Office 2003. You can use the following examples in calculated columns. Conditional formulas Check if a number is greater than or less than another number Use the IF function to do this task. Return a logical value after comparing column contents For a result that is a logical value (Yes or No), use the AND, OR, and NOT functions. For a result that is another calculation, or any other value other than Yes or No, use the IF, AND, and OR functions. Display zeroes as blanks or dashes Date and time formulas Add dates To add a number of days to a date, use the addition (+) operator.
Text Content Analyser Text Analyser - Text Analysis Tool Generate text statistics and analyse the content of a text. Use our free text analyser to generate a range of useful statistics about a text and calculate its readability scores. UsingEnglish.com is partnering with Gymglish to give you a free one-month trial of this excellent online English training course. Activate your free month of lessons (special offer for new users, with no obligation to buy) - and receive a level assessment! Sign Up Now! Text Analysis Tools Definition: Text analysis software enables users to determine the frequency with which words or phrases are used, create concordances, view words in context, and otherwise study patterns in texts. Tools: Resources: caqdas Networking Project: "We provide practical support, training and information in the use of a range of software programs designed to assist qualitative data analysis."KDNuggets: A text analysis software directoryTAPoR Text Analysis Recipes: Straightforward, step-by-step instructions for using text analysis tools to accomplish particular research tasks.WikiTADA: The collaborative website of the Text Analysis Developers Alliance. References: Evaluating the Quality of Electronic Texts, Lisa Spiro, director of the Digital Media Center at Fondren Library, Rice University. See Also:
Intro To Text Analysis With R Guest post by Christopher Johnson from www.codeitmagazine.com One of the most powerful aspects of using R is that you can download free packages for so many tools and types of analysis. Text analysis is still somewhat in its infancy, but is very promising. It is estimated that as much as 80% of the world’s data is unstructured, while most types of analysis only work with structured data. In this paper, we will explore the potential of R packages to analyze unstructured text. R provides two packages for working with unstructured text – TM and Sentiment. install.packages("devtools") require(devtools) install_url(" install_url(" install_url(" The remaining required packaged can be installed as follows. install.packages("plyr") install.packages("ggplot2") install.packages("wordcloud") y = NA
A T-SQL Regular Expression Library for SQL Server 2005. Free source code and programming articles Introduction With the advent of CLR integration into SQL Server 2005, it has become incredibly easy to extend the power of the T-SQL programming language. Two of the areas that can be improved upon by way of CLR integration are string matching and string manipulation. Background T-SQL has a handful of basic string matching functions (e.g. SQL Server 2005 now allows you to create user defined functions (among other things) using your .NET language of choice. Using the Code General Approach My objective here is to wrap some of the more commonly used static methods of the RegEx class in the .NET Framework into something useable in a T-SQL environment. Interface All four of the functions listed in this article share the same first two parameters: @Input NVARCHAR(MAX) This is the string to be analyzed. @Pattern NVARCHAR(MAX) This is the regular expression which will be executed against the @Input parameter. In addition, all four functions share the same last parameter. @IgnoreCase BIT Functions Name
Wakoopa - Tracking & Understanding service for competitors research, shows organic and Ads keywords for any site or domain Statistical Methods for Studying Literature Using R R is a powerful programing language for statistical analysis and visualization that can be broadly used for many applications in the digital humanities. As with any programming language, getting started with R involves a steep initial learning curve in order to produce useful results. In its current form, this blog contains the notes from a hands-on workshop that I initially ran at the University of Kansas's Digital Humanities Forum/THATCamp Representing Knowledge in the Digital Humanities in September of 2011 and expanded with a more literary focus at the (University of Kansas 2012 Digital Humanities Forum). The examples are based on three different data sets. Next: Getting Started With R -->>
Comunicación Empresarial Últimas publicaciones 7 de Abril de 2014 Día mundial de la Salud Edición papel Edición virtual Ver contenidos 4 de Abril de 2014 Calidad de vida Edición papel Edición virtual Ver contenidos 26 de Marzo de 2014 Empresas y emprendedores Edición papel Ver publicaciones anteriores Tweets por @1060com Prediction: Anchor Text is Dying...And Will Be Replaced by Co-citation - Whiteboard Friday The classic ranking signals: links, authority, keywords, and anchor text - have long remained dominant in Google's search rankings. But in the past few years, we've seen the rise of citations for local rankings, and now, we may be seeing more co-occurence of terms and links in search queries, in text content, and in links becoming more of a factor. p.s. I did this WB Friday in a bit of a rush, and will try to write more about co-occurence/co-citation in a more detailed future post. "Howdy, SEOmoz fans, and welcome to a second beardless edition of Whiteboard Friday.