Evaluating Performance of NBA Players with Sentiment Analysis on Twitter Messages
Pages 150 - 155
Abstract
Traditionally, we conduct polls to obtain people's opinions on certain subjects, but now as social media prevails, scientists can harvest people's opinions from the great amount of data generated from social media users. This paper performs sentiment analysis on the Twitter comments regarding NBA games to obtain public opinions on the NBA players as a new way of player-performance evaluation, instead of adopting the traditional way to assess players according to their statistics in the games or the poll results by the audience. The Twitter messages regarding 5 games during the 2019 NBA playoff finals are collected, and three types of sentiments (absolute, objective, and subjective sentiments) are extracted from these messages. This work explores which type of sentiment has the strongest correlation with the player performance and thus makes the best value to evaluate the player performance. Keywords are also extracted from the messages. Our findings suggest that subjective sentiment is the best value among the three types of sentiments.
References
[1]
Pak A, Paroubek P. (2010) Twitter as a corpus for sentiment analysis and opinion mining., 10(2010):1320-1326. Sam Anzaroot and Andrew McCallum. 2013. UMass Citation Field Extraction Dataset. Retrieved May 27, 2019 from http://www.iesl.cs.umass.edu/data/data-umasscitationfield
[2]
Das, S., Behera, R. K., kumar, M., & Rath, S. K. (2018). Real-time sentiment analysis of twitter streaming data for stock prediction. Procedia Computer Science, 132, 956–964.Chelsea Finn. 2018. Learning to Learn with Gradients. PhD Thesis, EECS Department, University of Berkeley.
[3]
Yoo, S. Y., Song, J. I., & Jeong, O. R. (2018). Social media contents based sentiment analysis and prediction system. Expert Systems with Applications, 105, 102–111.Matthew Van Gundy, Davide Balzarotti, and Giovanni Vigna. 2007. Catch me, if you can: Evading network signatures with web-based polymorphic worms. In Proceedings of the first USENIX workshop on Offensive Technologies (WOOT ’07) . USENIX Association, Berkley, CA, Article 7, 9 pages.
[4]
Jurivich, D. A., & Manocha, G. D. (2019). Opinion: Is twitter a new way to learn? Journal of Investigative Surgery, 34(5), 545–546. David Harel. 1979. First-Order Dynamic Logic. Lecture Notes in Computer Science, Vol. 68. Springer-Verlag, New York, NY. https://doi.org/10.1007/3-540-09237-4
[5]
Su Y J, Chen Y Q. (2014) NBA All-Star Prediction Using Twitter Sentiment Analysis[M]//Genetic and Evolutionary Computing. Springer, Cham, 2014: 193-201.
[6]
Houlihan, P., & Creamer, G. G. (2016). Risk premium of social media sentiment. SSRN Electronic Journal.
[7]
Crepeau, Richard C. (2001), NBA and NHL Playoffs - Return of Baseball. On Sport and Society. 610.
[8]
Younus, A., Qureshi, M. A., Asar, F. F., Azam, M., Saeed, M., & Touheed, N. (2011). What do the average twitterers say: A twitter model for public opinion analysis in the face of major political events. 2011 International Conference on Advances in Social Networks Analysis and Mining.
[9]
A. Khatua, E. Cambria, S. S. Ho and J. C. Na, "Deciphering Public Opinion of Nuclear Energy on Twitter," 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-8.
[10]
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37 (2): 267–307.
[11]
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '04.
[12]
Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V.-A., & Boyd-Graber, J. (2015). Beyond lda: Exploring supervised topic modeling for depression-related language in twitter. Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality
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- Evaluating Performance of NBA Players with Sentiment Analysis on Twitter Messages
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Published In
November 2021
172 pages
ISBN:9781450385060
DOI:10.1145/3501774
Copyright © 2021 ACM.
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Published: 26 March 2022
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