FOOTBALL MATCH OUTCOME PREDICTION USING SENTIMENT ANALYSIS OF TWITTER DATA
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Abstract
Twitter provides us with APIs for developers to engage with the Twitter platform.The biggest advantage in this is the Large Scale Machine Learning on twitter data. We use this Twitter API as a platform to extract tweets that can be used to predict football match outcomes. The extracted tweets are cleaned and structured.Sentiment analysis is performed on the these tweets by implementing SVM algorithm. Our main objective is to create a predictive model which also considers the odds-on favorite, players’ and teams’ current form to predict the outcome more efficiently. Taking all of these into consideration, we implement Text Mining, Sentiment Analysis and Machine Learning to predict the windraw-lose ratio of the teams and represent it graphically in the form of a pie chart.
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References
A. Joseph, N.E. Fenton, M. Neil - Predicting football results using Bayesian nets and other machine learning techniques - Knowledge-Based Systems- Elsevier(23 June 2006) [2] Rodrigo G. Martins, Alessandro S. Martins, Leandro A. Neves, Luciano V. Lima, Edna L. Flores, Marcelo Z. do Nascimento - Exploring polynomial classifier to predict match results in football championships- Elsevier Vol 83, 15 October 2017, Pages 79-93 (19 April 2017). [3] Adam Gledhill, Chris Harwood, Dale Forsdyke - A holistic perspective on career development in UK female soccer players: A negative case analysis- ElsevierVol 21, November 2015, Pages 65-77 (March 2017). [4] Ruchika Aggarwal, Latika Gupta- A Hybrid Approach for Sentiment Analysis using Classification AlgorithmIJCSMC, Vol. 6, Issue. 6, June 2017, pg.149 – 157 (June 2017). [5] Saurabh Vaidya HarshalSanghavi, India KushalGevaria - Football Match Winner Prediction - International Journal of Computer Applications (0975 – 8887) Vol 154 – No.3, (November 2016) [6] Chinwe Peace Igiri - Support Vector Machine–Based Prediction System for a Football Match Result - IOSRJCE Vol 17, Issue 3, Ver. III (May - Jun. 2015), PP 21-26 [7] Ulmer, B., Fernandez, M., & Peterson, M. (2013). Predicting Soccer Match Results in the English Premier League (Doctoral dissertation, Doctoral dissertation, Ph. D. dissertation, Stanford). [8] Jorge ValverdeRebaza, Alneu de Andrade Lopes - Exploiting behaviors of communities of twitter users for link prediction- Vol 3, Issue 4, pp 1063–1074, (December 2013) [9] V.Lakshmi, K.Harika , H.Bavishya, Ch.Sri Harsha SENTIMENT ANALYSIS OF TWITTER DATA – IRJET Volume: 04 Issue: 02 (Feb -2017) [10] Anika RehmanAhmad Ali- Sentiment Analysis on Twitter Data– IJACSA Vol. 8, No. 6, (2017) [11] EfthymiosKouloumpi, Theresa Wilson, Johanna Moore - Twitter Sentiment Analysis: The Good the Bad and the OMG! - Fifth International AAAI Conference (July 2011) [12] Kampakis, Stylianos, and Andreas Adamides. "Using Twitter to predict football outcomes." arXiv preprint arXiv:1411.1243 (2014). [13] Seyed-Ali Bahrainian, Andreas Dengel - Sentiment Analysis and Summarization of Twitter Data- IEEE 16th conference (2014) [14] Yaldo, L., Shamir, L - Computational Estimation of Football Player Wages – IJCSS Vol 16, Issue 1 (2017) [15] Hijmans, A., &Bhulai, S. (2017). Dutch football prediction using machine learning classifiers.