Machine learning for meeting analysis

Published

Conference Paper

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. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Duke Authors

Cited Authors

  • Kim, B; Rudin, C

Published Date

  • January 1, 2013

Published In

  • Aaai Workshop Technical Report

Volume / Issue

  • WS-13-17 /

Start / End Page

  • 59 - 61

International Standard Book Number 13 (ISBN-13)

  • 9781577356288

Citation Source

  • Scopus