TY - GEN
T1 - Situational estimation of sports broadcasting using a character level auto-encoder for live tweets
AU - Fujimoto, Nodoka
AU - Ushiama, Taketoshi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 19H04219.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Real-time videos enable people to experience real-world events in real time. However, it is challenging for people to keep watching an event when they are busy and have other activities to carry out. It is therefore crucial to develop a method for supporting the viewers' real-time video viewing effectively. Using sports broadcasting as a representative example of such videos, our goal is to develop a system that detects and automatically estimates the events in sports games that are likely to be of high value to the viewers and notifies them in real time during sports broadcasts. To this end, we propose a general-purpose method that efficiently models situations from small amounts of tweets without using domain-specific knowledge. The effectiveness of the method was verified via experiments by estimating the number of tweets posted from the modeled features of a situation with high accuracy.
AB - Real-time videos enable people to experience real-world events in real time. However, it is challenging for people to keep watching an event when they are busy and have other activities to carry out. It is therefore crucial to develop a method for supporting the viewers' real-time video viewing effectively. Using sports broadcasting as a representative example of such videos, our goal is to develop a system that detects and automatically estimates the events in sports games that are likely to be of high value to the viewers and notifies them in real time during sports broadcasts. To this end, we propose a general-purpose method that efficiently models situations from small amounts of tweets without using domain-specific knowledge. The effectiveness of the method was verified via experiments by estimating the number of tweets posted from the modeled features of a situation with high accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85114421936&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114421936&partnerID=8YFLogxK
U2 - 10.1109/WIIAT50758.2020.00045
DO - 10.1109/WIIAT50758.2020.00045
M3 - Conference contribution
AN - SCOPUS:85114421936
T3 - Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
SP - 316
EP - 322
BT - Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
A2 - He, Jing
A2 - Purohit, Hemant
A2 - Huang, Guangyan
A2 - Gao, Xiaoying
A2 - Deng, Ke
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
Y2 - 14 December 2020 through 17 December 2020
ER -