Advancing Arousal and Valence Prediction in Virtual Reality Through Sentiment Analysis Integration

This study investigated how integrating sentiment analysis derived from verbal responses to open-ended questions with physiological signals into support vector machine (SVM) models can enhance the prediction of arousal and valence in virtual reality (VR) gaming. Three VR Pong scenarios–slow, fast, and lag-inducing–were tested to observe the impact of game-play speed and responsiveness on sentiment scores. Participant responses were measured using the Self-Assessment Manikin for arousal and valence, and sentiment was analyzed from open-ended responses. Physiological measures, such as electrocardio-grams, galvanic skin responses, and electromyogram activity in forearm muscles, were also recorded. The results indicated that gameplay speed and responsiveness significantly affects player emotions, with faster games leading to higher sentiment scores. Additionally, incorporating these sentiment scores into SVM models improved the accuracy of valence and arousal predictions. This highlights the role of gameplay speed and responsiveness in influencing both emotional and physiological responses in VR and suggests that using diverse data types, including audio-derived sentiment analysis, could enhance the accuracy of affective computing models.