Contextual and active learning-based affect-sensing from virtual drama improvisation

Affect interpretation from open-ended drama improvisation is a challenging task. This article describes experiments in using latent semantic analysis to identify discussion themes and potential target audiences for those improvisational inputs without strong affect indicators. A context-based affect-detection is also implemented using a supervised neural network with the consideration of emotional contexts of most intended audiences, sentence types, and interpersonal relationships. In order to go beyond the constraints of predefined scenarios and improve the system's robustness, min-margin-based active learning is implemented. This active learning algorithm also shows great potential in dealing with imbalanced affect classifications. Evaluation results indicated that the context-based affect detection achieved an averaged precision of 0.826 and an averaged recall of 0.813 for affect detection of the test inputs from the Crohn's disease scenario using three emotion labels: positive, negative, and neutral, and an averaged precision of 0.868 and an average recall of 0.876 for the test inputs from the school bullying scenario. Moreover, experimental evaluation on a benchmark data set for active learning demonstrated that active learning was able to greatly reduce human annotation efforts for the training of affect detection, and also showed promising robustness in dealing with open-ended example inputs beyond the improvisation of the chosen scenarios.