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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Acknowledgment
The authors are grateful to Tecnologico de Monterrey, who through its Academic Scholarship Program for Graduate Students provided technical and financial support for the development of this research. In addition, we are grateful to CONACyT for the financial support awarded through the National Scholarship for PNPC and SNI programs designed for promoting quality research and close the existing gap between industry and academia.
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Miranda-Peña, C., Ceballos, H.G., Hervert-Escobar, L., Gonzalez-Mendoza, M. (2021). Predicting Soccer Results Through Sentiment Analysis: A Graph Theory Approach. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_32
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DOI: https://doi.org/10.1007/978-3-030-77980-1_32
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