Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Authors

  • Raza Ul Mustafa Department of Computer Science, COMSATS Institute of Information Technology, 57000 Sahiwal Campus, Pakistan
  • M. Saqib Nawaz Department of Information Science, School of Mathematical Sciences, Peking University, 100871 Beijing, China
  • M. Ikram Ullah Lali Department of Computer Science and Information Technology, University of Sargodha, 40100 Sargodha, Pakistan
  • Tehseen Zia Department of Computer Science, COMSATS Institute of Information Technology, 44000 Islamabad, Pakistan
  • Waqar Mehmood Department of Computer Science, COMSATS Institute of Information Technology, 47040 Wah Campus, Pakistan

DOI:

https://doi.org/10.22452/mjcs.vol30no1.5

Keywords:

Pattern Recognition, Cricket, Twitter, Sentimental Analysis, Opinion Mining

Abstract

Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

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Published

2017-03-01

How to Cite

Ul Mustafa, R., Nawaz, M. S., Ullah Lali, M. I., Zia, T., & Mehmood, W. (2017). Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods. Malaysian Journal of Computer Science, 30(1), 63–76. https://doi.org/10.22452/mjcs.vol30no1.5