research-article

Predicting Meeting Success With Nuanced Emotions

Authors:

Marios Constantinides,

Sagar Joglekar,

Daniele QuerciaAuthors Info & Claims

IEEE Pervasive Computing, Volume 21, Issue 2

Pages 51 - 59

https://doi.org/10.1109/MPRV.2022.3145047

Published: 01 April 2022 Publication History

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.

References

[1]

A. Akbik, T. Bergmann, D. Blythe, K. Rasul, S. Schweter, and R. Vollgraf, “Flair: An easy-to-use framework for state-of-the-art NLP,” in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics (Demonstrations), 2019, pp. 54–59.

[2]

B. A. Aseniero, M. Constantinides, S. Joglekar, K. Zhou, and D. Quercia, “MeetCues: Supporting online meetings experience,” in Proc. IEEE Visual. Conf., 2020, pp. 236–240.

[3]

C. Busso, P. G. Georgiou, and S. S. Narayanan, “Real-time monitoring of participants’ interaction in a meeting using audio-visual sensors,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2007, vol. 2, pp. II-685–II-688.

[4]

M. Constantinides, S. Šćepanović, D. Quercia, H. Li, U. Sassi, and M. Eggleston, “ComFeel: Productivity is a matter of the senses too,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 4, no. 4, pp. 1–21, 2020.

Digital Library

[5]

E. Coutinho and N. Dibben, “Psychoacoustic cues to emotion in speech prosody and music,” Cogn. Emotion, vol. 27, no. 4, pp. 658–684, 2013.

[6]

A. J. Cowell, et al., “Understanding the dynamics of collaborative multi-party discourse,” Inf. Visual., vol. 5, no. 4, pp. 250–259, 2006.

Digital Library

[7]

P. Gonzalez-Alonso, R. Vilar, and F. Lupiáñez-Villanueva, “Meeting technology and methodology into health big data analytics scenarios,” in Proc. IEEE 30th Int. Symp. Comput.-Based Med. Syst., 2017, pp. 284–285.

[8]

T. Hastie, S. Rosset, J. Zhu, and H. Zou, “Multi-class adaboost,” Statist. Interface, vol. 2, no. 3, pp. 349–360, 2009.

[9]

C. Hutto and E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proc. Int. AAAI Conf. Web Social Media, 2014, vol. 8, pp. 216–225.

[10]

V. Këpuska and G. Bohouta, “Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx),” Int. J. Eng. Res. Appl., vol. 7, no. 3, pp. 20–24, 2017.

[11]

C. D. Manning, M. Surdeanu, J. Bauer, J. R. Finkel, S. Bethard, and D. McClosky, “The stanford CoreNLP natural language processing toolkit,” in Proc. 52nd Annu. Meeting Assoc. Comput. Linguistics, Syst. Demonstrations, 2014, pp. 55–60.

[12]

P. Müller, M. X. Huang, and A. Bulling, “Detecting low rapport during natural interactions in small groups from non-verbal behaviour,” in Proc. 23rd Int. Conf. Intell. User Interfaces, 2018, pp. 153–164.

Digital Library

[13]

E. R. O’Neill, M. N. Parke, H. A. Kreft, and A. J. Oxenham, “Role of semantic context and talker variability in speech perception of cochlear-implant users and normal-hearing listeners,” J. Acoust. Soc. Amer., vol. 149, no. 2, pp. 1224–1239, 2021.

[14]

J. W. Pennebaker, R. L. Boyd, K. Jordan, and K. Blackburn, “The development and psychometric properties of LIWC2015,” Univ. Texas Austin, Austin, TX, USA, Tech. Rep., 2015.

[15]

J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proc. Conf. Empirical Methods Natural Lang. Process., 2014, pp. 1532–1543.

[16]

H. Rashkin, E. M. Smith, M. Li, and Y.-L. Boureau, “Towards empathetic open-domain conversation models: A new benchmark and dataset,” in Proc. 57th Annu. Meeting Assoc. Comput. Linguistics, 2019, pp. 5370–5381.

[17]

S. Somasundaran, J. Ruppenhofer, and J. Wiebe, “Detecting arguing and sentiment in meetings,” in Proc. 8th SIGdial Workshop Discourse Dialogue, 2007, pp. 26–34.

[18]

M. Sundermeyer, R. Schlüter, and H. Ney, “LSTM neural networks for language modeling,” in Proc. 13th Annu. Conf. Int. Speech Commun. Assoc., 2012, pp. 194–197.

[19]

T. Yoshioka, et al., “Advances in online audio-visual meeting transcription,” in Proc. IEEE Autom. Speech Recognit. Understanding Workshop, 2019, pp. 276–283.

[20]

N. Zhang, T. Zhang, I. Bhattacharya, H. Ji, and R. J. Radke, “Visualizing group dynamics based on multiparty meeting understanding,” in Proc. Conf. Empirical Methods Natural Lang. Process., 2018, pp. 96–101.

Cited By

Park GPanda PTankelevitch LRintel S(2024)The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video MeetingsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661507(1638-1657)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661507
Hosseinkashi YTankelevitch LPool JCutler RMadan C(2024)Meeting Effectiveness and Inclusiveness: Large-scale Measurement, Identification of Key Features, and Prediction in Real-world Remote MeetingsProceedings of the ACM on Human-Computer Interaction10.1145/36373708:CSCW1(1-39)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637370
Das Swain VGao LWood WMatli SAbowd GDe Choudhury M(2023)Algorithmic Power or Punishment: Information Worker Perspectives on Passive Sensing Enabled AI Phenotyping of Performance and WellbeingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581376(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581376
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Predicting Meeting Success With Nuanced Emotions

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Published In

cover image IEEE Pervasive Computing

IEEE Pervasive Computing Volume 21, Issue 2

April-June 2022

98 pages

ISSN:1536-1268

Issue’s Table of Contents

1536-1268 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 April 2022

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Cited By

Park GPanda PTankelevitch LRintel S(2024)The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video MeetingsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661507(1638-1657)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661507
Hosseinkashi YTankelevitch LPool JCutler RMadan C(2024)Meeting Effectiveness and Inclusiveness: Large-scale Measurement, Identification of Key Features, and Prediction in Real-world Remote MeetingsProceedings of the ACM on Human-Computer Interaction10.1145/36373708:CSCW1(1-39)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637370
Das Swain VGao LWood WMatli SAbowd GDe Choudhury M(2023)Algorithmic Power or Punishment: Information Worker Perspectives on Passive Sensing Enabled AI Phenotyping of Performance and WellbeingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581376(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581376
Constantinides MQuercia D(2022)The Future of Hybrid MeetingsProceedings of the 1st Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3533406.3533415(1-6)Online publication date: 8-Jun-2022
https://dl.acm.org/doi/10.1145/3533406.3533415

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