Predicting Meeting Success With Nuanced Emotions

Ke Zhou, Marios Constantinides, Sagar Joglekar, Daniele Quercia

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

While current meeting tools are able to capture key analytics (e.g., transcript and summarization), they do not often capture nuanced emotions (e.g., disappointment and feeling impressed). Given the high number of meetings that were held online during the COVID-19 pandemic, we had an unprecedented opportunity to record extensive meeting data with a newly developed meeting companion application. We analyzed 72 h of conversations from 85 real-world virtual meetings and 256 self-reported meeting success scores. We did so by developing a deep-learning framework that can extract 32 nuanced emotions from meeting transcripts, and by then testing a variety of models predicting meeting success from the extracted emotions. We found that rare emotions (e.g., disappointment and excitement) were generally more predictive of success than more common emotions. This demonstrates the importance of quantifying nuanced emotions to further improve productivity analytics, and, in the long term, employee well-being.

Original languageEnglish
Pages (from-to)51-59
Number of pages9
JournalIEEE PERVASIVE COMPUTING
Volume21
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • Linguistics
  • Predictive models
  • Principal component analysis
  • Psychology
  • Standards
  • Task analysis
  • Training

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