Computational Modeling of Players’ Emotional Response Patterns to the Story Events of Video Games

This study suggests an approach for the computational modeling of players' emotional response patterns to story events in video games. We propose what is termed the dynamic narrative emotion model for analyzing the emotional response patterns of video game players by combining and reconstructing the OCC cognitive emotion model and D. Price's emotional intensity equation. Based on this model, we compared with two emotional response patterns of players to story events in both commercially successful and unsuccessful video games. The analysis was conducted with 360 emotional response values extracted from the playing experiences of 10 player's. The results showed that responses from commercially successful games are 3.3 times higher in terms of the frequency of emotion transitions, 1.3 times in terms of the number of emotion types, and twice as high in terms of the distance of an emotion transition compared to those of unsuccessful games. The results of this study can be applied by game designers with two implications for creating story-driven video games; first, to differentiate the emotional responses patterns of players in successful games from unsuccessful games, and second, to develop emotional transition strategies when designing story events in accordance with feedback from the players' emotional response patterns while playing the games.