Predicting Group Satisfaction in Meeting Discussions

We address the task of automatically predicting group satisfaction in meetings using acoustic, lexical, and turn-taking features. Participant satisfaction is measured using post-meeting ratings from the AMI corpus. We focus on predicting three aspects of satisfaction: overall satisfaction, participant attention satisfaction, and information overload. All predictions are made at the aggregated group level. In general, we find that combining features across modalities improves prediction performance. However, feature ablation significantly improves performance. Our experiments also show how data-driven methods can be used to explore how different facets of group satisfaction are expressed through different modalities. For example, inclusion of prosodic features improves prediction of attention satisfaction but hinders prediction of overall satisfaction, but the opposite for lexical features. Moreover, feelings of sufficient attention were better reflected by acoustic features than by speaking time, while information overload was better reflected by specific lexical cues and turn-taking patterns. Overall, this study indicates that group affect can be revealed as much by how participants speak, as by what they say.