Analysis and prediction of tennis players' match performance with sentiment analysis

Recent studies have shown that players' pregame emotions can be an important data source when predicting NBA players' game performance, and that sentiment states have an impact on athlete's game performance. Our project aimed to determine whether a similar phenomenon could be observed in professional tennis players: sentiments states influence game performance. To do this, we use web crawlers to obtain tennis match data, track the tweets of participating tennis players, collect tweet content from posts posted on their Twitter accounts. We used natural language processing for sentiment. We used the VADER sentiment analysis tool to sentiment-analyze the content of their tweets. In this way, they can identify their emotional state before the game and count the number of correlations to determine whether their emotional state is related to the performance of the game. Through experiments, we can conclude that the emotional state of athletes is closely related to their sports performance. Next, we combine the sentiment analysis data with other behavioral data of the athletes to build a model that predicts the outcome of the game based on a decision tree algorithm. Using the model to predict the performance of players in the game with an accuracy rate of 85%, it provides guidance for tennis players' teams in future training plans and game plans.