ワークショップ (国際) A Case Study of In-House Competition for Ranking Constructive Comments in a News Service
Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita (Tokyo Institute of Technology), Kazuma Murao (Visits Technologies Inc.), Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura (Tokyo Institute of Technology), Satoshi Sekine (RIKEN AIP)
The 9th International Workshop on Natural Language Processing for Social Media (SocialNLP 2021)
Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience. Research on ranking comments with different metrics to measure the comment quality has shown ''constructiveness'' used in argument analysis is promising from a practical standpoint. In this paper, we report a case study in which this constructiveness is examined in the real world. Specifically, we examine an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service.