Abstract
Player evaluation is a key component of the question-answering (QA) system in sports. Since existing player evaluation methods heavily rely on game statistics, they cannot capture the qualitative impact of each player during a game, which can be exploited using news articles after the game. In this paper, we propose a deep learning-based player evaluation model by combining both quantitative game statistics and the qualitative analyses provided by news articles. Players are classified as positive or negative based on their performance during certain periods, and news articles in the same period are annotated using the player's class. Then, the relationship between news articles and the annotated polarity is investigated by a deep neural network, which can deal with the high dimensionality of the text data. Since there is no explicit polarity label for news articles, we use the change in game statistics in target periods to annotate related sentences. The proposed system is applied to a Korean professional baseball league (KBO) and it is shown to be capable of understanding the sentence polarity of news articles on player performances.
| Original language | English |
|---|---|
| Journal | Knowledge-Based Systems |
| DOIs | |
| Publication status | Accepted/In press - 2017 |
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Keywords
- Baseball
- Deep neural network
- Sentence polarity
- Sports player evaluation
ASJC Scopus subject areas
- Software
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence
Cite this
A deep learning-based sports player evaluation model based on game statistics and news articles. / Park, Young Joon; Kim, Hyung Seok; Kim, Donghwa; Lee, Hankyu; Kim, Seoung Bum; Kang, Pilsung.
In: Knowledge-Based Systems, 2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A deep learning-based sports player evaluation model based on game statistics and news articles
AU - Park, Young Joon
AU - Kim, Hyung Seok
AU - Kim, Donghwa
AU - Lee, Hankyu
AU - Kim, Seoung Bum
AU - Kang, Pilsung
PY - 2017
Y1 - 2017
N2 - Player evaluation is a key component of the question-answering (QA) system in sports. Since existing player evaluation methods heavily rely on game statistics, they cannot capture the qualitative impact of each player during a game, which can be exploited using news articles after the game. In this paper, we propose a deep learning-based player evaluation model by combining both quantitative game statistics and the qualitative analyses provided by news articles. Players are classified as positive or negative based on their performance during certain periods, and news articles in the same period are annotated using the player's class. Then, the relationship between news articles and the annotated polarity is investigated by a deep neural network, which can deal with the high dimensionality of the text data. Since there is no explicit polarity label for news articles, we use the change in game statistics in target periods to annotate related sentences. The proposed system is applied to a Korean professional baseball league (KBO) and it is shown to be capable of understanding the sentence polarity of news articles on player performances.
AB - Player evaluation is a key component of the question-answering (QA) system in sports. Since existing player evaluation methods heavily rely on game statistics, they cannot capture the qualitative impact of each player during a game, which can be exploited using news articles after the game. In this paper, we propose a deep learning-based player evaluation model by combining both quantitative game statistics and the qualitative analyses provided by news articles. Players are classified as positive or negative based on their performance during certain periods, and news articles in the same period are annotated using the player's class. Then, the relationship between news articles and the annotated polarity is investigated by a deep neural network, which can deal with the high dimensionality of the text data. Since there is no explicit polarity label for news articles, we use the change in game statistics in target periods to annotate related sentences. The proposed system is applied to a Korean professional baseball league (KBO) and it is shown to be capable of understanding the sentence polarity of news articles on player performances.
KW - Baseball
KW - Deep neural network
KW - Sentence polarity
KW - Sports player evaluation
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UR - http://www.scopus.com/inward/citedby.url?scp=85031092498&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2017.09.028
DO - 10.1016/j.knosys.2017.09.028
M3 - Article
AN - SCOPUS:85031092498
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
SN - 0950-7051
ER -