Abstract
More than four out of 10 sports fans consider themselves soccer fans, making the game the world’s most popular sport. Sports are season based and constantly changing over time, as well, statistics vary according to the sport and league. Understanding sports communities in Social Networks and identifying fan’s expertise is a key indicator for soccer prediction. This research proposes a Machine Learning Model using polarity on a dataset of 3,000 tweets taken during the last game week on English Premier League season 19/20. The end goal is to achieve a flexible mechanism, which automatizes the process of gathering the corpus of tweets before a match, and classifies its sentiment to find the probability of a winning game by evaluating the network centrality.
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CITATION STYLE
Miranda-Peña, C., Ceballos, H. G., Hervert-Escobar, L., & Gonzalez-Mendoza, M. (2021). Predicting Soccer Results Through Sentiment Analysis: A Graph Theory Approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12747 LNCS, pp. 422–435). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-77980-1_32
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