We present experimental results on the task of automatically predicting group members' attitudes about management of their meeting, based on linguistic and acoustic features derived from the meeting recordings and transcripts. The group members' attitudes were gathered from detailed post-meeting questionnaires. A key finding is that features of linguistic content by themselves yield poor prediction performance on this task, but the best results are found by combining acoustic and linguistic features in a multimodal prediction model. When trying to automate the detection of group member attitudes that might be manifested subtly in their language and behaviour, a multimodal analysis is key.
CCS CONCEPTS• Computing methodologies → Natural language processing; Machine learning approaches; • Human-centered computing;