World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans' tweets

The present project collected real-time tweets from U.S. soccer fans during five 2014 FIFA World Cup games (three games between the U.S. team and another opponent and two games between other teams) using Twitter search API. We used sentiment analysis to examine U.S. soccer fans' emotional responses in their tweets, particularly, the emotional changes after goals (either own or the opponent's). We found that during the matches that the U.S. team played, fear and anger were the most common negative emotions and in general, increased when the opponent team scored and decreased when the U.S. team scored. Anticipation and joy were also generally consistent with the goal results and the associated circumstances during the games. Furthermore, we found that during the matches between other teams, U.S. tweets showed more joy and anticipation than negative emotions (e.g., anger and fear) and that the patterns in response to goal or loss were unclear. This project revealed that sports fans use Twitter for emotional purposes and that the big data approach to analyze sports fans' sentiment showed results generally consistent with the predictions of the disposition theory when the fanship was clear and showed good predictive validity.