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Due to the nature of Twitter, tweets are able to provide real-time data for various world occurrences. We decided to focus on sports, since we believed that the tweets posted by the users watching sporting events can provide valuable insight to key events. Due to the wide range of sports that exist in modern world, we decided to limit our research to the most commonly followed game, soccer. In order to accomplish this, tweets were collected from Twitter’s streaming API and placed into a MongoDB in which tweets were filtered by official game hashtags and team names. By performing a post-hoc analysis of spikes in the volume of tweets, we were able to detect match events such as goal scored, the scorer’s name, the scorer’s team, and penalties throughout the game. Likewise, we tested the use of sentiment analysis to further correlate the relationship of the tweets and overall match progress.

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