Super Bowl LI
Over the past week, there has been an abundance of social media chatter about which team will take the 2017 Super Bowl trophy. This chatter is comprised of everything including predictive analysis by professionals, groups of fans arguing back and forth, and of course the individuals who are simply posting online to show their support (or contempt).
Looking through this all gives us an interesting perspective on which team fans believe are most likely to win. Check out the graph below to see for yourself:
Will this method of congregating and analyzing the predictions of others give us enough insight to know who will take home the trophy? We’ll find out tomorrow!
We’ll make this short and sweet. Here’s our sentiment graph for the 2016 US Presidential election as of 6pm CST today (11/08/16) before most polls closed. The graph up to November 7th was also given to our friends at WCIA Channel 3 and was reported in the morning news. We’ll also be going on tomorrow at 9:30 am for some post-election talk after all the dust clears and we officially know who our next president is.
A holistic approach to this year’s election
Our data scientist, Mariah, has been working on some cool stuff for the presidential election. Specifically to do with correlations and comparisons to real world events and testing our graphs against traditional polls. We’re always open to constructive criticism and it’s how we grow as a company. Here’s what she had to say:
The main goal of my work this summer with Tweetsense is to compare their results to polls for the upcoming election. I run Tweetsense’s algorithm each day and then once polls are released, I record the poll results and can compare the two to see if the two considerably different methods yield similar results. There are a few caveats to account for, however. First, the results from Tweetsense are on a scale of -1 to 1, and strong positive sentiment tweets can cancel out strong negative sentiment tweets, giving an overall average score, so it’s not enough just to look at an average and call it a day (a common statistical proverb)
In polling for favorability, the classical polls give out a percentage of people who view a candidate favorably and a percentage for negative views. Another problem to deal with is that voter representation will never be perfect regardless of the method, lest it be somehow omniscient. As you can imagine, many of Clinton’s supporters are not on Twitter, but a lot of Sanders supporters (who will likely vote for her in November) are on Twitter, and they’re not saying great things about her. If they’re saying anything good, it’s not with enthusiasm. Trump supporters, on the other hand, are more evenly distributed among all ages and many of them are on Twitter. And more on this, third party candidates (Gary Johnson and Jill Stein) don’t have many saying bad things about them because few people know who they are but their supporters are strongly favoring them on Twitter. Tweetsense doesn’t conclude opinions using the volume of tweets, but shows opinion data instead. Therefore, popular opinions show Gary Johnson will sweep everyone, though we know that’s highly unlikely simply because of the way the electoral college works.
Tweetsense’s sentiment predictions as of late. Major world events labelled on the graph for convenience. Work and credit: Mariah Yelenick