Predicting Meeting Success With Nuanced Emotions
IEEE Pervasive Computing
; 2022.
Article
in English
| Scopus | ID: covidwho-1731035
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. IEEE
Linguistics; Predictive models; Principal component analysis; Psychology; Standards; Task analysis; Training; Deep learning; Job analysis; Predictive analytics; Productivity; 'current; Learning frameworks; Meeting tools; Meeting transcripts; Principal-component analysis; Real-world; Virtual meetings
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
IEEE Pervasive Computing
Year:
2022
Document Type:
Article
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