Abstract
We aimed to develop a software tool that can determine the “tone of a room” in virtual meetings. Our prototype, Kaleidoscope, uses preexisting machine learning and deep learning libraries to analyze the expressed emotions from individuals in an online meeting, and turn that data into an overall analysis of group tone through novel analytic tools. Results from human participants, asked to identify emotions observed from online meeting videos, are used to validate and refine the prototype. The Kaleidoscope prototype currently achieves an overall accuracy greater than 70%, more than double that of human-to-human agreement; and demonstrates the unique capability of identifying the group tone of a meeting. Future extensions could provide facilitators real-time emotion tracking and group tone assessment, paving the way for technology-assisted interventions to combat group polarization and other problematic group dynamics.
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