Michigan's Trey Burke, Source: http://isportsweb.com/2011/11/15/michigan-basketball-trey-burke-leads-wolverines-victory/
The researchers limited the tweets they looked at to teams which were ranked in the top 25 of the AP poll at the time the data was collected, as well as top scorers from across college basketball. The researchers were able to data-mine almost 500,000 tweets in the week-long period and they show some interesting results when breaking the sentiment analysis down by team and player. Unsurprisingly, Michigan's Trey Burke was the leader in positive sentiment analysis during the time period. This should not be a ground-breaking revelation because at the conclusion of the season he was named the National Player of the Year by the AP, and anyone who watched him carry Michigan to the national title game the past 2+ weeks knows how good he is. Apparently though, being good means getting a lot of Twitter love. Most of the players heading the largest sentiment graphic were well-known stars, although I did notice an absence of players from smaller schools having big years, such as Creighton's Doug McDermott. I guess when you are not on TV every week the Twitterverse isnt all that interested in you. The other player I found a surprising amount of love for was Kentucky center Nerlens Noel. For those of you who don't know, Noel injured a knee earlier in the year and was not even playing at the time the data was mined, but still got a surprising amount of sentiment. My only guess is that UK fans were collectively whining about how the would have made the tourney had he not been injured.
Sad UK Fan, Source: http://www.crimsoncast.com/2012/03/pre-game-meal-kentucky-wants-revenge/imagescau0mx6c-2/
Data about individual teams is only briefly mentioned in the article, with only a supporting graphic showing the sentiment for the Kansas Jayhawks, but the authors did include a note that traditional powerhouse teams led the way in tweet volume, which was not at all surprising. The final thing that really surprised me was the volume of tweets coming from overseas, particularly London. The United Kingdom is not generally know as a basketball fan country, sticking mostly to soccer, but tweets out of London outpaced many major American cities, which was surprising to say the least. Even more impressive is that most people there are asleep when games are being played over here, meaning the Brits are dedicated enough to check up on happenings around college hoops the next day, then tweet about it retroactively. I got some cool insights out of this article, and it concludes with a challenge to all of us to try and use their HP Vertica platform to generate a model combining Twitter sentiment analysis and statistics to try and predict the winner. Its a little late to try it out until next year, but I will be keeping a close eye on their blog to see if anyone gave it their best shot.
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ReplyDeleteReading this article about sentiment analysis of basketball winners and stars made me wonder what else sentiment analysis could be used for. When I looked up sentiment analysis on Google, I found a lot of articles about using it to predict Oscar winners. I think that the article by Mary C. Long that I read contained a wise statement when it she said, “Twitter can’t who will win, but it can tell us who should win.” This statement was made because the people who were polled actually have no influence on the winners, but they do represent popular vote, and popular vote is what we all believe should represent everything, right? This article includes a link to a visualization by Topsy and Twitter that shows how the people in the Twittersphere were selecting their winners in the six main categories day by day.
ReplyDeleteWhile the people who are tweeting about these topics (basketball, the Oscars, etc.) may be representative of who will actually win, the tweets essentially represent the popularity of the team, actor, etc. Twitter and Topsy are both great tools to show us what is “trending” all around the world according to those who tweet, but they cannot necessarily predict the future.
Checking out the Twitter Oscar’s Index visualization that I mentioned is definitely worthwhile, so you should check it out! I also did a tutorial a while back about how to use Topsy, so you can even try your own sentiment analysis if you want!
Source: http://www.mediabistro.com/alltwitter/advanced-sentiment-analysis-tracker_b34380
Aside from the games themselves, the Kevin Ware injury was wasily the most talked about event. Ware went from not having a twitter to having an absurd number of followers in a span of 48 hours. I read the article and saw that the data and article were both from before the injury occurred. It would be real interesting to see how it changed from the first few weeks to the end of the tournament.
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