Analysis of Football Data on Twitter for Popularity Mapping and Transfer Predictions
Keywords:Tweepy, TextBlob, Sentiment Analysis, Text Classification, SVM Algorithm, Tableau
Twitter has gathered a reputation of being a reliable source for predictive modeling on various domains such as flu trends, sports data, political trends etc. Football is widely considered to be the most popular sport in the world. We introduce a novel approach of analyzing the tweets collected over a period of time which are related to football. We present a visualized world map with the high density areas indicating the parts of the world where football is most popular. Further, we incorporate the fan opinions of popular football players by analyzing their tweets individually. This ultimately leads to prediction of player movements from his current club to another club in the transfer window. Hence, this model helps in identifying the popularity trends in football around the world and also increases the role fans play within the club they support.
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