Interaction Process Label Recognition in Group Discussion

In qualifying and analyzing the performance of group interaction, interaction processing analysis (IPA) defined by Bale is considered a useful approach. IPA is a system for labeling a total of 12 interaction categories for the interaction process. Automatic IPA can manually encompass the gap in spending manpower and can efficiently qualify group performance. In this paper, we present computational interaction processing analysis by developing a model to recognize categories of IPA. We extract both verbal features and nonverbal features for IPA category recognition modeling with SVM, RF, DNN and LSTM machine learning algorithms and analyze the contribution of multimodal features and unimodal features for the total data and each label. We also investigate the effect of context information by training sequences with different lengths with an LSTM and evaluating them. The results show that multimodal features achieve the best performance with an F1 score of 0.601 for the recognition of 12 IPA categories using the total data. Multimodal features are better than the unimodal features for the total data and most labels. The results of investigating context information show that a suitable length of sequence enables a longer sequence to achieve the best Fl score of 0.602 and a better performance for recognition.