Using system and user performance features to improve emotion detection in spoken tutoring dialogs
Hua Ai, Diane J. Litman, Kate Forbes-Riley, Mihai Dragos Rotaru, Joel Tetreault, Amruta Purandare · 2006
In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Our results show a relative improvement of 2.7% on classification accuracy and 8.08% on Kappa over using standard lexical, prosodie, sequential, and identification features. This level of improvement is comparable to the performance improvement shown in previous studies by applying dialog acts or lexical/prosodic-/discourse- level contextual features.