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.
A couple weeks ago, we released Version 1.51 of the Wolfram Cloud. We’ve made quite a few significant functionality improvements even since 1.50—a major milestone from many months of hard work—as we continue to make cloud notebooks as easy and powerful to use as the notebooks on our desktop clients for Wolfram|One and Mathematica. You can read through everything that’s new in 1.51 in the detailed release notes. After working on this version through to its release, I’m excited to show off Wolfram Cloud 1.51—I’ve put together a few of the highlights and favorite new features for you here.
Mark Greenberg is a retired educator and contributor to the Tech-Based Teaching blog, which explores the intersections between computational thinking, edtech and learning. He recounts his experience adapting old game code using the Wolfram Language and deployment through the Wolfram Cloud.
Chicken Scratch is an academic trivia game that I originally coded about 20 years ago. At the time I was the Academic Decathlon coach of a large urban high school, and I needed a fun way for my students to remember thousands of factoids for the Academic Decathlon competitions. The game turned out to be beneficial to our team, and so popular that other teams asked to buy it from us. I refreshed the questions each year and continued holding Chicken Scratch tournaments at the next two schools I worked in.
✕ net = NetModel["ResNet-101 Trained on ImageNet Competition Data"]