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Analyzing the Impact of Political Messages with the Wolfram Language

Much effort and money are spent trying to analyze whether political messages resonate with the electorate. With the UK in its final days before a general election, I thought I would see if I could gain such insight with minimal effort.

My approach is simple: track the sentiment of tweets that mention each party. Since the Wolfram Language has a built-in sentiment classifier and connections to external services, we can analyze these messages with only a few lines of code.

Jon McLoone, Director, Technical Communication & Strategy
Computation & Analysis

Building a Lattice Boltzmann–Based Wind Tunnel with the Wolfram Language

My student days learning fluid dynamics were all about studying complicated equations and various methods of simplifying and manipulating these equations to get some kind of a result. Unfortunately, this left very little to the imagination when it came to getting an intuitive feel for how a fluid would behave in different situations. When I took my first experimental fluid dynamics course, I got to see how one would use different visualization techniques to understand qualitatively the behavior of the flow. These visualizations gave me a way of creatively looking at a flow, and, as an added bonus, they looked stunning. All these experiments and visualizations were being carried out inside a wind tunnel.

Paritosh Mokhasi, Kernel Developer, Algorithms R&D
Computation & Analysis

Envisioning City Spaces, Aligning DNA Sequences, Classifying Emotional Speech and More: Wolfram Community Highlights

In this roundup of our recent Wolfram Community favorites, our talented users explore different methods of accessing, interpreting and representing data—creating some eye-catching results that offer new ways of looking at the world. We’re also excited to showcase a few projects from alumni of our annual Wolfram High School Summer Camp and Wolfram Summer School. Check out the culmination of their hard work, as well as how Community members find clever solutions using the Wolfram Language.

Chapin Langenheim, Editor & Web Project Coordinator, Project Management
Education & Academic

Powering Engineering Education with Wolfram Virtual Labs

Explore the contents of this article with a free Wolfram SystemModeler trial. How can you make teaching come alive and be more engaging? For many educators, the answer turns out to be not so much a single solution, but rather a set of tools that can vary according to subject and even by student. So today, I want to add something new to the pedagogical toolkit: Wolfram Virtual Labs.

Wolfram Virtual Labs are open educational resources in the form of interactive courseware that are used to explain different concepts in the classroom. Our ambition is to provide an easy way to study difficult concepts and promote student curiosity.

For this post, I spoke with Dr. Matteo Fasano about his experience with using Virtual Labs as a course complement in the masters’ courses in which he acts as a teaching assistant. He also told me why and how he supported the Wolfram MathCore group to develop the CollegeThermal Virtual Labs (now available) and how they can help teachers or instructors make learning more engaging.

Ankit Naik, Sales Engineer, Wolfram MathCore Matteo Fasano, Postdoctoral Researcher, Politecnico di Torino, Italy
Computation & Analysis

Deploying Bandpass Filters Using the Wolfram Language Microcontroller Kit

Real-time filters work like magic. Usually out of sight, they clean data to make it useful for the larger system they are part of, and sometimes even for human consumption. A fascinating thing about these filters is that they don’t have a big-picture perspective. They work wonders with only a small window into the data that is streaming in. On the other hand, if I had a stream of numbers flying across my screen, I would at the very least need to plot it to make sense of the data. These types of filters are very simple as well.

Suba Thomas, Software Engineer, Algorithms R&D
Current Events & History

Authorship Forensics from Twitter Data

A Year Ago Today

On September 5 of last year, The New York Times took the unusual step of publishing an op-ed anonymously. It began “I Am Part of the Resistance inside the Trump Administration,” and quickly became known as the “Resistance” op-ed. From the start, there was wide‐ranging speculation as to who might have been the author(s); to this day, that has not been settled. (Spoiler alert: it will not be settled in this blog post, either. But that’s getting ahead of things.) When I learned of this op-ed, the first thing that came to mind, of course, was, “I wonder if authorship attribution software could....” This was followed by, “Well, of course it could. If given the right training data.” When time permitted, I had a look on the internet into where one might find training data, and for that matter who were the people to consider for the pool of candidate authors. I found at least a couple of blog posts that mentioned the possibility of using tweets from administration officials. One gave a preliminary analysis (with President Trump himself receiving the highest score, though by a narrow margin—go figure). It even provided a means of downloading a dataset that the poster had gone to some work to cull from the Twitter site.

The code from that blog was in a language/script in which I am not fluent. My coauthor on two authorship attribution papers (and other work), Catalin Stoean, was able to download the data successfully. I first did some quick validation (to be seen) and got solid results. Upon setting the software loose on the op-ed in question, a clear winner emerged. So for a short time I “knew” who wrote that piece. Except. I decided more serious testing was required.

Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D
Computation & Analysis

Embracing Uncertainty: Better Model Selection with Bayesian Linear Regression

Readers who follow the Mathematica Stack Exchange (which I highly recommend to any Wolfram Language user) may have seen this post recently, in which I showed a function I wrote to make Bayesian linear regression easy to do. After finishing that function, I have been playing around with it to get a better feel of what it can do, and how it compares against regular fitting algorithms such as those used by Fit. In this blog post, I don’t want to focus too much on the underlying technicalities (check out my previous blog post to learn more about Bayesian neural network regression); rather, I will show you some of the practical applications and interpretations of Bayesian regression, and share some of the surprising results you can get from it.

Sjoerd Smit, Wolfram Europe
Computation & Analysis

Taking the Cerne Abbas Walk: From Conceptual Art to Computational Art

Cerne Abbas Walk is an artwork by Richard Long, in the collection of the Tate Modern in London and on display at the time of this writing. Several of Long’s works involve geographic representations of his walks, some abstract and some concrete. Cerne Abbas Walk is described by the artist as “a six-day walk over all roads, lanes and double tracks inside a six-mile-wide circle centred on the Giant of Cerne Abbas.” The Tate catalog notes that “the map shows his route, retracing and re-crossing many roads to stay within a predetermined circle.”

The Giant in question is a 180-foot-high chalk figure carved into a hill near the village of Cerne Abbas in South West England. Some archaeologists believe it to be of Iron Age pedigree, some think it to date from the Roman or subsequent Saxon periods and yet others find the bulk of evidence to indicate a 17th-century origin as a political satire. (I find the last theory to be both the most amusing and the most convincing.)

I found the geographic premise of Cerne Abbas Walk intriguing, so I decided to replicate it computationally.

Jesse Friedman, Software Engineer, Engine Connectivity Engineering
Computation & Analysis

The Ultimate Team Generator with the Wolfram Language

Every summer, I play in a recreational Ultimate Frisbee league—just “Ultimate” to those who play. It’s a fun, relaxed, coed league where I tend to win more friends than games.

The league is organized by volunteers, and one year, my friend and teammate Nate was volunteered to coordinate it. A couple weeks before the start of the season, Nate came to me with some desperation in his voice over making the teams. The league allows each player to request to play with up to eight other players—disparagingly referred to as their “baggage.” And Nate discovered that with over 100 players in a league, each one requesting a different combination of teammates, creating teams that would please everyone seemed to become more complicated by the minute.

Luckily for him, the Wolfram Language has a suite of graph and network tools for things like social media. I recognized that this seemingly overwhelming problem was actually a fairly simple graph problem. I asked Nate for the data, spent an evening working in a notebook and sent him the teams that night.

Bob Sandheinrich, Development Manager, Document & Media Systems
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