What’s the most positive or negative religion? — Sentiment and Data Analysis of Holy Books with R. Wikileaks: a diez años del sismo político del Cablegate, EE. UU. sigue en la mira. La noticia cayó como un rayo el 28 de noviembre de 2010.
Cinco importantes medios occidentales comenzaron a publicar simultáneamente secretos de la sala de máquinas de la diplomacia de Washington. El material: exactamente 251.287 documentos, en su mayoría secretos y confidenciales, del Departamento de Estado de la superpotencia, que ofrecían una imagen sin adornos de la política exterior estadounidense en documentos provenientes de embajadas estadounidenses en todo el mundo. La plataforma Wikileaks los hizo accesibles. Nunca antes habían caído tantos secretos a la vez en manos de los periodistas. El socio alemán de Wikileaks fue la revista Der Spiegel, que se refirió a una "catástrofe mayúscula” para la política exterior de Estados Unidos". De "asesinato colateral" a Cablegate "Desde nuestro punto de vista, los despachos de la embajada fueron un punto culminante de las revelaciones de Wikileaks en 2010", recuerda el periodista de Spiegel Marcel Rosenbach en conversación con DW.
MonkeyLearn - Text Mining: The Beginner's Guide. What is Text Mining?
Text mining, also known as text analysis, is the process of transforming unstructured text data into meaningful and actionable information. Text mining utilizes different AI technologies to automatically process data and generate valuable insights, enabling companies to make data-driven decisions. For businesses, the large amount of data generated every day represents both an opportunity and a challenge. On the one side, data helps companies get smart insights on people’s opinions about a product or service. Categorización de conflictos sociales en el ámbito de los recursos naturales: un estudio de las actividades extractivas mediante la minería de textos. Mediante la aplicación de técnicas de minería de textos, se desarrolló una metodología para medir el número de conflictos sociales relacionados con la explotación de recursos naturales no renovables.
Técnicas de Procesamiento del Lenguaje Natural en la Recuperación de Información. Transkribus. Disclaimer Offenlegung nach § 25 des österreichischen Mediengesetzes Medieninhaber Leopold-Franzens-Universität Innsbruck Herausgeber und verantwortlich für den Inhalt Digitalisierung und elektronische Archivierung – DEA Universität Innsbruck Innrain 52 – 6020 Innsbruck – Österreich Telefon: ++43-(0)512-507-8451 E-Mail: email@example.com Webmaster Umsatzsteueridentifikationsnummer (UID) der Universität Innsbruck: ATU57495437 Die Universität ist laut Universitätsgesetz 2002 von der Umsatzsteuer befreit. Die Inhalte der Webseiten von wurden sorgfältig geprüft und bearbeitet. The Classical Language Toolkit. OCR4all : software de reconocimiento de texto de código abierto de documentos históricos. PLOS Collections: Article collections published by the Public Library of Science.
Getting Started with Text Preprocessing for Machine Learning & NLP. Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic.
Machine learning has been used to automatically translate long-lost languages. The other script, Linear B, is more recent, appearing only after 1400 BCE, when the island was conquered by Mycenaeans from the Greek mainland.
Evans and others tried for many years to decipher the ancient scripts, but the lost languages resisted all attempts. The problem remained unsolved until 1953, when an amateur linguist named Michael Ventris cracked the code for Linear B. His solution was built on two decisive breakthroughs. First, Ventris conjectured that many of the repeated words in the Linear B vocabulary were names of places on the island of Crete.
That turned out to be correct. His second breakthrough was to assume that the writing recorded an early form of ancient Greek. Ventris’s work was a huge achievement. It’s not hard to imagine that recent advances in machine translation might help. Enter Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google’s AI lab in Mountain View, California. First some background. These vectors obey some simple mathematical rules. La estilometría – UniCo. 03 Abr ¿Qué es la estilometría?
Using Data to Find the Angriest Death Grips Song. Text analysis, wordcount, keyword density analyzer, prominence analysis. Stylometry with R: A Package for Computational Text Analysis. McKee Ch1. Introduction to Text Analytics with R: Overview. Text Mining with R. R Programming/Text Processing. This page includes all the material you need to deal with strings in R.
The section on regular expressions may be useful to understand the rest of the page, even if it is not necessary if you only need to perform some simple tasks. This page may be useful to : perform statistical text analysis.collect data from an unformatted text file.deal with character variables. In this page, we learn how to read a text file and how to use R functions for characters.
There are two kind of function for characters, simple functions and regular expressions. Help.search(keyword = "character", package = "base") Text Mining with R [Book] R’s tidytext turns messy text into valuable insight. “Many of us who work in analytical fields are not trained in even simple interpretation of natural language,” write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach.
The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document’s most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they’re using text mining on job postings at Stack Overflow, some of the challenges and best practices they’ve experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative.
Let’s start with the basics. Linguistic Inquiry and Word Count. Laurence Anthony's Software. FireAnt (Filter, Identify, Report, and Export Analysis Toolkit) is a freeware social media and data analysis toolkit with built-in visualization tools including time-series, geo-position (map), and network (graph) plotting.
[FireAnt Homepage] [Screenshots] [Help] PayPal Donations and Patreon Supporters: Click one of the following if you want to make a small donation to support the future development of this tool. A Statistical Analysis of the Work of Bob Ross. Bob Ross was a consummate teacher.
He guided fans along as he painted “happy trees,” “almighty mountains” and “fluffy clouds” over the course of his 11-year television career on his PBS show, “The Joy of Painting.” In total, Ross painted 381 works on the show, relying on a distinct set of elements, scenes and themes, and thereby providing thousands of data points. I decided to use that data to teach something myself: the important statistical concepts of conditional probability and clustering, as well as a lesson on the limitations of data. So let’s perm out our hair and get ready to create some happy spreadsheets! More Culture What I found — through data analysis and an interview with one of Ross’s closest collaborators — was a body of work that was defined by consistency and a fundamentally personal ideal. I analyzed the data to find out exactly what Ross, who died in 1995, painted for more than a decade on TV.
Conditional probability can be a bit tricky. What about footy little hills? Stanford Literary Lab. Humanities Data in R. Discourse analysis. Discourse analysis (DA), or discourse studies, is a general term for a number of approaches to analyze written, vocal, or sign language use, or any significant semiotic event. Discourse analysis has been taken up in a variety of social science disciplines, including linguistics, education, sociology, anthropology, social work, cognitive psychology, social psychology, area studies, cultural studies, international relations, human geography, communication studies, and translation studies, each of which is subject to its own assumptions, dimensions of analysis, and methodologies. Topics of interest Topics of discourse analysis include: Political discourse Text analysis, wordcount, keyword density analyzer, prominence analysis.
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). It was further revised for an additional workshop at the University of Iowa Oberman Center for Advanced Study in the fall of 2014. The examples are based on three different data sets. 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. » 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. There is enough information to give you a good start using R. “This book is an essential resource for anyone who wants to study literature using computational methods.” Text Analyzer - Text analysis Tool - Counts Frequencies of Words, Characters, Sentences and Syllables. TAPoR: Text Analysis Tools. Romancing the Novel: Large Scale Text Analysis in the Humanities (by Mark Algee-Hewitt) Large-Scale Text Analysis with R - HILT 2015. Text mining, the practice of using computational and statistical analysis on large collections of digitized text, is becoming an increasingly important way of extracting meaning from writing.
Whether working on survey data, medical records, political speeches or even digitized collections of historical writing, we are now able to use the power of computational algorithms to extract patterns from vast quantities of textual data. This technique gives us information we could never access by simply reading the texts. But determining which patterns have meaning and which answer key questions about our data is a difficult task, both conceptually and methodologically; particularly for those who work in the humanities who are able to benefit the most from these methods.
Searching; Visualized: “The Book History Bibliograph” Tagged with: collaboration, database, libraries, literary topology, text analysis, virtual environments Posted in Cultural Archives & Curation, Knowledge Environments, Major Projects, Projects, Research, Tools Research, Visualization With the increased interest in the material aspects of the book, the field of book history has seen rapid expansion in the past twenty years. An extremely broad area of research, the problem of finding sources in multiple languages and disciplines has been of continuing concern.
“The Book History Bibliograph”, a bibliographic tool currently under development between Stanford and the University of Edinburgh, with input from McGill, proposes creative solutions to cross-disciplinary and multi-lingual searching. Under the direction of Dr. Tom Mole (University of Edinburgh), the SSHRC-funded project is one of many initiatives supported by the “Interacting with Print” group at McGill.
Currently in “beta”, the Bibliograph database contains about 500 sources; Dr. Automated Data Collection with R: A Practical Guide to Web Scraping and Text ... - Simon Munzert, Christian Rubba, Peter Meißner, Dominic Nyhuis - Google Books. Text Analysis with R for Students of Literature. From the book reviews: “This is a well written book on the topic of Text Analysis. There is enough information to give you a good start using R.
Followed by easy to understand details about text analysis. … This is a good book to have if you are doing text analysis.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, August, 2014) “A remarkably well-crafted book that will allow students to get a quick start and progress toward quite sophisticated text mining tasks. … exercises provided at the end of each chapter, with solutions at the end of the book, should serve well to help students solidify their knowledge and gain more confidence in their text mining skills. … a great addition to the libraries of digital humanists and natural language enthusiasts who wish to expand their programming literacy … .”