Evaluating Performance of NBA Players with Sentiment Analysis on Twitter Messages

Qiwen Li, Jiarui Zhang, Jiayu Guo, Jiaqi Li, Chenhao Kang
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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.
用推特信息情感分析评价NBA球员的表现
传统上,我们通过民意调查来获取人们对某些主题的意见,但现在随着社交媒体的盛行,科学家可以从社交媒体用户产生的大量数据中收集人们的意见。本文通过对NBA比赛的Twitter评论进行情感分析,以获取公众对NBA球员的评价,这是一种新的球员表现评价方式,而不是传统的根据球员在比赛中的统计数据或观众的投票结果来评价球员。收集了2019年NBA季后赛5场比赛的推特信息,从中提取了绝对情绪、客观情绪和主观情绪三种情绪。这项工作探讨了哪种类型的情绪与玩家表现的相关性最强,从而为评估玩家表现创造了最佳价值。还从消息中提取关键字。我们的研究结果表明,主观情绪是三种情绪中最具价值的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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