Machine Learning for Meeting Analysis

Been Kim, Cynthia Rudin

Abstract: Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: i) there are common macro-patterns in the way social dialogue acts are interspersed throughout a meeting, and ii) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker.