Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) |
| Editors | Tao Yue, Mehdi Mirakhorli |
| Publisher | IEEE Computer Society |
| Pages | 371-376 |
| Number of pages | 6 |
| ISBN (electronic) | 9781665418980 |
| Publication status | Published - 2021 |
| Event | 29th IEEE International Requirements Engineering Conference Workshops, REW 2021 - Virtual, Notre Dame, United States Duration: 20 Sept 2021 → 24 Sept 2021 Conference number: 29 |
Publication series
| Name | Proceedings of the IEEE International Conference on Requirements Engineering |
|---|---|
| Volume | 2021-September |
| ISSN (Print) | 1090-705X |
| ISSN (electronic) | 2332-6441 |
Abstract
Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.
Keywords
- affect, development team, Interaction analysis, meeting, sentiment analysis, software project
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Engineering(all)
- General Engineering
- Business, Management and Accounting(all)
- Strategy and Management
Cite this
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2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). ed. / Tao Yue; Mehdi Mirakhorli. IEEE Computer Society, 2021. p. 371-376 (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2021-September).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - From Textual to Verbal Communication
T2 - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021
AU - Herrmann, Marc
AU - Klünder, Jil
N1 - Conference code: 29
PY - 2021
Y1 - 2021
N2 - Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.
AB - Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.
KW - affect
KW - development team
KW - Interaction analysis
KW - meeting
KW - sentiment analysis
KW - software project
UR - http://www.scopus.com/inward/record.url?scp=85118442260&partnerID=8YFLogxK
U2 - 10.1109/REW53955.2021.00065
DO - 10.1109/REW53955.2021.00065
M3 - Conference contribution
AN - SCOPUS:85118442260
T3 - Proceedings of the IEEE International Conference on Requirements Engineering
SP - 371
EP - 376
BT - 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)
A2 - Yue, Tao
A2 - Mirakhorli, Mehdi
PB - IEEE Computer Society
Y2 - 20 September 2021 through 24 September 2021
ER -