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

Ning Xinyi; Luo Dan; Zhang Shang

doi:10.1117/12.3026323

27 March 2024 Analysis and prediction of tennis players' match performance with sentiment analysis

Ning Xinyi, Luo Dan, Zhang Shang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051D (2024) https://doi.org/10.1117/12.3026323
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ning Xinyi, Luo Dan, and Zhang Shang "Analysis and prediction of tennis players' match performance with sentiment analysis", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051D (27 March 2024); https://doi.org/10.1117/12.3026323

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KEYWORDS

Data modeling

Decision trees

Education and training

Emotion

Acquisition tracking and pointing

Mathematical optimization

Statistical analysis

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