In this talk Professor Schumaker, who teaches at Central Connecticut State University, will try to answer the question: “Can the sentiment contained in tweets serve as a meaningful proxy to predict match outcomes and if so, can the magnitude of these outcomes be similarly predicted based on the degree of sentiment?”
This talk should be an excellent place to learn about some of the key areas the initiative is trying to explore–big data and text analysis. The predictive power of sentiment analysis has been a consistent element of Schumaker’s work which he has applied to the stock market as well as sports.
There will be some refreshments available and we’ve found that a Friday afternoon talk can be a nice way to end the week. What’s more, this talk give you just enough time to work things out before the Saturday morning kickoffs and certainly before Chelsea goes to the Arsenal that Sunday.
Can the sentiment contained in tweets serve as meaningful proxy to predict match outcomes and if so, can the magnitude of these outcomes be similarly predicted based on the degree of sentiment? To answer these questions we constructed the CentralSport system to gather Tweets from the English Premier League and analyze their sentiment content for use in predicting match outcomes. From our analysis, we found that the models incorporating positive tweets were easier to profit from (All Positive model netted a $3,375.18 excess return). Looking deeper into the models we found point spread prediction was possible. Clubs with 1,000 or more negative tweets than their rival would typically lose by 1 goal (observed 65.2% of the time). Clubs with 2,000 to 10,000 more positive tweets would win by 1 goal (56.25%) and 10,000+ positive tweets would win by 2+ goals (100%). These results demonstrate the power of hidden information contained within tweet sentiment and has implications on wagering systems.
Friday, April 24th at 4:15 in Usdan 108