Evaluating Performance of NBA Players with Sentiment Analysis on Twitter Messages

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.