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Laurence Anthony's Software

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.

http://www.laurenceanthony.net/software.html

Related:  Text AnalysisData-driven history / DigHum10carlaResearch methods

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.

My Best of series You can find all of my “Best” lists in broad categories here. The link to that page can also be found at the top right of my blog: My Best Of Series I also have them all on another page where they are listed in the chronological order in which I originally posted them. You can find that link at the top of my blog by first clicking on About and then scrolling down to Websites of the Year. Two thousand “Best” lists are a lot of best lists! Of course, Control + F on PCs and Command + F on Macs are great ways to search for keywords on those lists when you’re looking for something.

"Examining the Potential of Combining the Methods of Grounded Theory an" by Shalini Lal, Melinda Suto et al. Abstract Increasingly, qualitative researchers are combining methods, processes, and principles from two or more methodologies over the course of a research study. Critics charge that researchers adopting combined approaches place too little attention on the historical, epistemological, and theoretical aspects of the research design. Rather than discounting eclecticism in qualitative research, we prefer to place it on a continuum of integration whereby at the ideal end of the spectrum, the researcher demonstrates thorough knowledge of the approaches being drawn from and a thoughtful consideration of the rationale for combining methods. However, there is limited reflection in the literature on the combination of methods from specific methodological approaches.

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. Why would an analyst mine text? What insights can be derived from mining instances of words, sentiment of words?

2018 Managing 100 Digital Humanities Projects: Digital Scholarship & Archiving in King’s Digital Lab James Smithies, King's College London; Carina Westling, King's College London; Anna-Maria Sichani, King's College London; Pam Mellen, King's College London; Arianna Ciula, King's College London Modelling Medieval Hands: Practical OCR for Caroline Minuscule Origin and meaning of idiosyncrasy by Online Etymology Dictionary idiosyncrasy (n.) Related Entries syn-idiomidiosyncraticrare Share Link copied! Grounded Theory: A Down-to-Earth Explanation – Research Methodology in Education Introduction & History Sociologists Barney G. Glaser and the late Anselm L. Strauss developed grounded theory in the late 1960s.

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.

Examples of Root Words Many of the words we use come from a root word. Once you pull off any prefixes or suffixes, the root will be normally at the front or the back of the remaining word. A little digging will uncover just what the root word really means. For example, in a word such as scissors, the root word is sciss, which means cut. Determining a Root Word How to conduct a qualitative meta-analysis: Tailoring methods to enhance methodological integrity: Psychotherapy Research: Vol 28, No 3 Although qualitative research has long been of interest in the field of psychology, meta-analyses of qualitative literatures (sometimes called meta-syntheses) are still quite rare. Like quantitative meta-analyses, these methods function to aggregate findings and identify patterns across primary studies, but their aims, procedures, and methodological considerations may vary. Objective: This paper explains the function of qualitative meta-analyses and their methodological development. Recommendations have broad relevance but are framed with an eye toward their use in psychotherapy research.

Text Mining with R In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of items even when we’re not sure what we’re looking for. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language. As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book.

Reference Services and Sources Learn about: Reference services, selecting the right reference source, types of reference sources, where and how to find reference sources, and reference sources available via the Web. Reference Services The function of libraries is three-fold. Libraries acquire information, organize that information in a way it can be retrieved, and disseminate the information the library has acquired. Reference services fulfills this last function.

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