本研究使用支援向量迴歸 ( support vector regression, SVR ) ，針對音樂遊戲玩家的情感建立預測模型。根據心流模型理論 ( flow model ) 遊戲中的主要情感有樂趣 ( Fun ) 、挑戰 ( Challenge ) 、挫折 ( Frustration ) 、可預測性 ( Predictability ) 、焦慮 ( Anxiety ) 、無聊 ( Boredom ) 六個情感因素，遊戲設計師可以進行情感遊戲的設計。而本研究將使用感性工學的理論，先將遊戲特徵進行參數化並製作成遊戲樣本後，結合遊戲中會出現之六個情感因素做成情感問卷調查，再利用支援向量迴歸建立預測模型，毎一組訓練樣本將遊戲的參數作為輸入值，問卷所得的情感分數為輸出值。遊戲設計師可以利用本研究提出的情感預測模型來掌握玩家的情感，作為設計遊戲時的依據。

This study uses support vector regression (SVR) and constructs a predictive model aimed at music game player. According to flow model theory, emotions in games mainly include six affective factors: Fun, Challenge, Frustration, Predictability, Anxiety and Boredom. Game designers can design emotion game according to flow theory. Using theories of Kansei Engineering, this study first parameterizes game features, produces game samples, and makes emotion questionnaire survey in combination with six affective factors appearing in the game. Then, SVR is used to establish predictive model; each group of training sample adopts game parameters as input and emotion questionnaire scores as output. Game designers can utilize the predictive model for emotion proposed in this research to grasp players’ mood and feelings, and consider this model as a basis of game design.

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